110101: General Mathematics and Computational Science I

Short Name: 
GenMathCPS I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
Yes 
General Mathematics and Computational Science I and II
are the introductory first year courses for students in Mathematics and Applied and Computational Mathematics. In addition, these courses address anyone with an interest in
mathematics and mathematical modeling. Each semester includes a
selection of ``pure'' and ``applied'' topics which provide a solid
foundation for further study, convey the pleasure of doing
mathematics, and relate mathematical concepts to realworld
applications.Topics covered in the first semester are:
 Fundamental concepts: sets, relations, functions,
equivalence classes.
 Numbers: Peano axioms, proof by induction, construction
of integers and rational numbers.
 Discrete Mathematics: combinatorics, binomial
coefficients, generating functions, applications to elementary
discrete probability.
 Inequalities: Geometricarithmetic mean inequalities,
Cauchy inequality; Laplace's method and Stirling's
approximation.
 Difference equations: linear first order difference
equations, nonlinear first order difference equations, equilibrium
points and their stability, linear second order difference
equations; modeling with difference equations.
110102: General Mathematics and Computational Science II

Short Name: 
GenMathCPS II 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
110101 

Corequisites: 
None 

Tutorial: 
Yes 
This course continues General Mathematics and Computational Science
I with the following selection of topics:
 Groups: Basic properties and simple examples, Euclidean
symmetries of the plane, symmetry groups of subsets of the plane,
symmetry groups of polyhedra.
 Graph Theory: Graphs and parity, trees, Euler's formula
and Euler characteristic, pairings, Eulerian graphs.
 Stochastic Modeling: Simple discrete stochastic systems,
continuum limits, introduction to entropy.
 Linear Programming: graphical method, simplex method,
duality.
 Fourier Transform: Discrete Fourier transform, fast
Fourier transform, Fourier transform on groups.
110111: Natural Science Lab Unit  Symbolic Software

Short Name: 
NatSciLab SymbSoft 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The Natural Science Lab Units in Mathematics and ACM will introduce
the computer as a tool for the working mathematician, as well as
for scientists in many other fields.The Lab Unit Symbolic Software introduces
Mathematica, a software package that can perform complex
symbolic manipulations such as solving algebraic equations, finding
integrals in closed form, or factoring mathematical expressions.
Mathematica also has powerful and flexible graphing capabilities
that are useful for illustrating concepts as well as numerical
data. The computer will be used as a tool in this course so that
you will also learn some mathematics alongside learning to use the
computer program.
110112: Natural Science Lab Unit  Numerical Software

Short Name: 
NatSciLab NumSoft 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The Natural Science Lab Units in Mathematics and ACM will introduce
the computer as a tool for the working mathematician, as well as
for scientists in many other fields.The Lab Unit Numerical Software introduces Matlab
and its free cousin Octave, software packages that allow
easy and in many cases efficient implementations of matrixbased
``number crunching''. The software is ideal for numerical work such
as solving differential equations or analyzing large amounts of
laboratory data. The computer will be used as a tool in this course
so that you will also learn some mathematics alongside learning to
use the computer program.
This Lab Unit is particularly suited for students from both
schools interested in experiments, as Matlab is used as a
standard tool for analyzing and visualizing data in many fields of
research. 1#1 10202
110231: Nonlinear Dynamics Lab

Short Name: 
NLDLab 

Type: 
Lab 

Credit Points: 
7.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The Nonlinear Dynamics Lab is an introduction to a variety of
nonlinear phenomena and chaos through experiments. Most experiments
will be done in a virtual laboratory, your laptop, but we will also
include a few ``wetlab'' experiments. Programming environments
will be Scientific Python for number crunching and Mathematica for
symbolic computing.Topics include nonlinear electric oscillators, coupled pendula,
and pattern formation in chemical reactions. A main focus of the
lab is the development of standard tools for the numerical solution
of differential equations, the application of automated tools for
bifurcation analysis, and continuation methods. We will also
implement simple agentbased models and pseudospectral PDE solvers
for reactiondiffusion equations.
The lab is accessible to second and third year students in
Physics, Mathematics, and EE/CS who have completed the recommended
course load of these majors. Prerequisite is a willingness to learn
about differential equations and the associated calculus.
110221: Derivatives Lab

Short Name: 
DerivLab 

Type: 
Lab 

Credit Points: 
7.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The lab gives a first practical introduction to stochastic
processes and to the pricing of derivative assets in finance.Topics include an introduction to finance (bonds, yields,
immunization), binomial tree models, discrete Brownian paths,
stochastic ODEs, MonteCarlo methods, finite differences solutions
for the BlackScholes equation, and an introduction to time series
analysis, parameter estimation, and calibration. Students will
program and explore all basic techniques in a numerical programming
environment and apply these algorithms to real data whenever
possible.
110262: Applied Differential Equations and Modeling

Short Name: 
ApplDEMod 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
120101 

Corequisites: 
None 

Tutorial: 
No 
This course offers an introduction to ordinary differential
equations and their applications. Mathematical modeling of
continuoustime dynamics has its origins in classical mechanics but
is now prevalent in all areas of physical and life sciences.
Attempting to solve such problems often leads to a differential
equation. Consequently, a variety of analytical and numerical
methods have been developed to deal with various classes of
equations and initial value problems, the most important of which
is the class of linear equations. Other methods (such as Laplace
transform) for solving many differential equations of special form
will also be discussed. The course underlines the importance of
qualitative analysis of differential equations, with a discussion
of simple models such as the LotkaVolterra equation.All students in the School of Engineering and Science with and
interest in the application of Mathematics to reallife problems,
and have a mathematical background equivalent to either Engineering
and Science Mathematics 1B (Multivariable Calculus, ODE) or
Analysis I/II should consider taking this course as a home school
elective. Students of ACM can take this course as part of their
second year major requirements.
110301: Introduction to parallel programming with MPI and
OpenMP

Short Name: 
MPI/Open MP Workshop 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
120202 

Corequisites: 
None 

Tutorial: 
No 
This intersession workshop is a practical introduction to parallel
programming. The focus is on the MessagePassing Interface (MPI)
which is the standard programming method for parallel computers
with distributed memory, in particular PCclusters. The last day of
the workshop is devoted to OpenMP which is used to program
computers with shared memory.The workshop comprises lectures and handson MPI programming
sessions.
110341: Numerical Analysis

Short Name: 
NumAnal 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
This course an advanced introduction to Numerical Analysis. It
complements ESM 4A  Numerical Methods, placing emphasis, on the one hand, on the analysis of numerical
schemes, on the other hand, focusing on problems faced in
largescale computations. Topics include sparse matrix linear
algebra, large scale and/or stiff systems of ordinary differential
equations, and a first introduction to methods for partial
differential equations.
110361: Mathematical Modeling in Biomedical Applications

Short Name: 
MathMod BioMed 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
The course discusses the area of mathematical modeling in
biomedical applications. It includes an introduction into the basic
principles of mathematical modeling, and it covers a variety of
models for growth and treatment of cancer with increasing
complexity ranging from simple ordinary differential equations to
more complicated free boundary problems and partial differential
equations. Further models for the description of physiology in the
human body like blood flow and breathing are briefly touched as
well.
110391: Guided Research Applied and Computational Mathematics
I

Short Name: 
GR ACM I 

Type: 
Self Study 

Credit Points: 
7.5 

Prerequisites: 
Permission of instructor 

Corequisites: 
None 

Tutorial: 
No 
Guided Research allows study, typically in the form of a research
project, in a particular area of specialization that is not offered
by regularly scheduled courses. Each participant must find a member
of the faculty as a supervisor, and arrange to work with him or her
in a small study group or on a oneonone basis.Guided research has three major components: Literature study,
research project, and seminar presentation. The relative weight of
each will vary according to topic area, the level of preparedness
of the participant(s), and the number of students in the study
group. Possible research tasks include formulating and proving a
conjecture, proving a known theorem in a novel way, investigating a
mathematical problem by computer experiments, or studying a problem
of practical importance using mathematical methods.
Third year students in Mathematics and ACM may take two
semesters of Guided Research. The Guided Research report in the
spring semester will typically be the Bachelor Thesis which is a
graduation requirement for every undergraduate. Note that the
Bachelor Thesis may also be written as part of any other course by
arrangement with the respective instructor of record.
Students are responsible for finding a member of the faculty as
a supervisor and report the name of the supervisor and the project
title to the instructor of record no later than the end of
Week 4. A semester plan is due by the end of Week 6.
110392: Guided Research and BSc Thesis in Applied and Computational
Mathematics II

Short Name: 
GR ACM II 

Type: 
Self Study 

Credit Points: 
7.5 

Prerequisites: 
Permission of instructor 

Corequisites: 
None 

Tutorial: 
No 
As for Guided Research Applied and Computational
Mathematics I.
120101: ESM 1A  Single Variable Calculus

Short Name: 
ESM 1A 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
Yes 
The courses from the Engineering and Science Mathematics 1
series provide the foundation for all other Engineering and Science
Mathematics courses. Taking at least one of them is mandatory for
all Engineering and Science majors. Emphasis is on the use of basic
mathematical concepts and methods in the sciences, rather than on
detailed proofs of the underlying mathematical theory.The course ESM 1A covers basic differential and integral
calculus of functions of one variable. It starts with a brief
review of number systems and elementary functions, then introduces
limits (for both sequences and functions) and continuity, and
finally derivatives and differentiation with applications (tangent
problem, error propagation, minima/maxima, zerofinding, curve
sketching). A short chapter introduces complex numbers.
The second half of the semester is devoted to integration
(antiderivatives and Riemann integral) with applications, and
concluded by brief introductions to scalar separable and linear
firstorder differential equations, and the convergence of
sequences and power series.
Compared to ESM 1C which covers similar material, this course
assumes a rigorous high school preparation in Mathematics and
leaves more room for explaining mathematical concepts (as needed
for the majority of SES majors).
120111: ESM 1B  Multivariable Calculus, ODE

Short Name: 
ESM 1B 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
120101 

Tutorial: 
Yes 
Engineering and Science Mathematics 1B introduces multivariable
calculus and ordinary differential equations, topics of particular
importance to the physical sciences. Students of ACM, Physics, and
Electrical Engineering are strongly encouraged to take this course
in their first semester. The curriculum is designed so that ESM 1A
and ESM 1B can be taken at the same time.The course covers vector algebra (threedimensional vectors, dot
product, cross product), equations of lines, planes, and spheres,
Euclidean distance, vectorvalued functions, space curves,
functions of several variables, partial derivatives, chain rule,
gradient, directional derivative, extrema, Lagrange multipliers,
double and triple integrals with applications, change of variables,
vector fields, divergence, curl, cylindrical and spherical
coordinates, line integrals, Green's theorem in the plane, surface
and volume integrals, divergence theorem, Stokes' theorem,
introduction to ordinary differential equations (direction field,
the question of existence and uniqueness of solutions), separable
and exact equations, integrating factors, and linear higher order
ODEs with constant coefficients.
120102: ESM 2A  Linear Algebra, Probability, Statistics

Short Name: 
ESM 2A 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
Yes 
Second semester Engineering and Science Mathematics is offered in
two parallel classes that cover a common set of core topics at
approximately the same level of difficulty. However, style of
exposition and selection of additional material will vary slightly
to meet the needs of different groups of majors.ESM 2A is recommended for students majoring in Life Sciences or
Chemistry. It covers the following topics: Linear Algebra
(equations of lines and planes, matrix algebra, system of linear
equations, matrix inverse, vector spaces, linear independence,
basis, dimension, linear transformations, change of basis,
eigenvalues and eigenvectors, diagonalization). Probability (basic
notions of set theory, outcomes, events, sample space, probability,
conditional probability, Bayes' rule, permutations and
combinations, random variables, expected value, variance, binomial,
Poisson, and normal distributions, central limit theorem).
Statistics (onesample hypothesis testing, two sample hypothesis
testing, chisquare hypothesis testing, analysis of variance,
bivariate association, simple linear regression, multiple
regression and correlation).
120112: ESM 2B  Linear Algebra, Fourier, Probability

Short Name: 
ESM 2B 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
120101 or 120111 or 120121 

Corequisites: 
None 

Tutorial: 
Yes 
Second semester Engineering and Science Mathematics is offered in
two parallel classes that cover a common set of core topics at
approximately the same level of difficulty. However, style of
exposition and selection of additional material will vary slightly
to meet the needs of different groups of majors.ESM 2B is recommended for students who do not intend to major in
the Life Sciences or Chemistry. It covers the following topics:
 Linear Algebra (equations of lines and planes, matrix algebra,
system of linear equations, matrix inverse, vector spaces, linear
independence, basis, dimension, linear transformations, change of
basis, eigenvalues and eigenvectors, diagonalization, inner
products, orthonormalization)
 Fourier methods (expanding functions in terms of orthonormal
function systems, Fourier series, Fourier transform, Dirac
deltafunction)
 Probability (basic notions of set theory, outcomes, events,
sample space, probability, conditional probability, Bayes' rule,
permutations and combinations, random variables, expected value,
variance, binomial, Poisson, and normal distributions, central
limit theorem).
120201: ESM 3A  Advanced Linear Algebra, Stochastic Processes

Short Name: 
ESM 3A 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
120102 or 120112 

Corequisites: 
None 

Tutorial: 
No 
Engineering and Science Mathematics 3A is mandatory for students in
Electrical Engineering and Computer Science, and is also
recommended as a home school elective for students who would like
to learn more advanced topics from Linear Algebra and Probability
than were covered in second semester Engineering and Science
Mathematics.The course covers matrix factorizations such as Jordan normal
form, QR, and SVD with their typical applications, for example, to
leastsquares and lowrank approximation problems. It deepens the
understanding of discrete and continuous random variables and
vectors (joint and conditional distributions and moments,
correlation and covariance, generating functions), of sums of
i.i.d. random variables and limit theorems, and introduces to the
basic types of stochastic processes (Markov chains, Poisson
process, Wiener process) and their properties.
120211: ESM 3B  Complex Variable Calculus, PDE

Short Name: 
ESM 3B 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
120102 or 120112 

Corequisites: 
None 

Tutorial: 
No 
Engineering and Science Mathematics 3B is mandatory for students of
Physics and for some students in Applied and Computational
Mathematics (please consult program handbook).The course covers the CauchyRiemann equations, singularities
and zeros, branch cuts, potential theory, conformal
transformations, complex integrals, Cauchy's theorem and integral
formula, Taylor and Laurent series, residue theorem with
applications; the basic linear PDEs (wave, heat, Laplace equation),
linear first order PDEs, inhomogeneous and second order equations,
characteristics, uniqueness, separation of variables, transform
methods, an introduction to Green's functions, the Dirichlet and
Neumann problems, and an outlook on nonlinear PDEs.
120202: ESM 4A  Numerical Methods

Short Name: 
ESM 4A 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
120112 or 100221 

Corequisites: 
None 

Tutorial: 
No 
Engineering and Science Mathematics 4A is mandatory for students of
Electrical Engineering, Computer Science, and Applied and
Computational Mathematics. It is also recommended as a home school
elective for students who would like to get a short, onesemester
introduction to Numerical Methods.This course is a handson introduction to numerical methods. It
covers root finding methods, solving systems of linear equations,
interpolation, numerical quadrature, solving ordinary differential
equations, the fast Fourier transform, and optimization. These
methods are crucial for anyone who wishes to apply mathematics to
the real world, i.e. computer scientists, electrical engineers,
physicists and, of course, mathematicians themselves.
100211: Analysis I

Short Name: 
Analysis I 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
Yes 
Analysis I/II is one of the fundamental courses in the
mathematical education (together with Linear Algebra I/II). Its
goal is to develop calculus in a rigorous manner and in sufficient
generality to prepare the student for advanced work in mathematics.
At the same time, the content is chosen so that students arrive
quickly at central concepts which are used in essentially all
mathematics courses, and which are needed in the exact sciences.The Analysis sequence begins with a quick review of natural,
rational and real numbers (which are assumed as known), and
introduces the field of complex numbers. The axiom of completeness
distinguishes the real numbers from the rationals and marks the
beginning of Analysis. The complex exponential and trigonometric
functions are defined.
Metric spaces are introduced and used to define continuity and
convergence in a general framework. The BolzanoWeierstraß
and the HeineBorel theorems are proved. The intermediate and
maximal value theorems for functions on the real line are discussed
as consequences of connectedness and compactness on metric spaces.
Sequences of functions are discussed, in particular uniform
convergence, as well as the continuity, differentiability,
integrability of the limit function.
Differentiability of functions on the real line is introduced.
The mean value theorem and Taylor's theorem is discussed.
The Riemann integral in one variable is introduced. The relation
between the derivative and the integral, i.e., the fundamental
theorem of calculus is proved.
This course has no formal prerequisites; incoming students with
a strong mathematics background are encouraged to take this class
in their first semester. However, a familiarity with mathematical
reasoning and proof (e.g. proof by induction or by contradiction),
such as introduced in General Mathematics and
Computational Science I, is required.
100212: Analysis II

Short Name: 
Analysis II 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100211 

Corequisites: 
100221 or 120102 or 120112 (if not
already taken) 

Tutorial: 
Yes 
This course is a continuation of Analysis I. Its main theme is to extend the concepts from Analysis I, in
particular differentiation and integration, to functions of several
variables. Taylor's theorem in several variables, the implicit
function theorem and the inverse function theorem are proved.
(Riemann) integration in several real variables is introduced,
including the transformation formula for integrals in several
variables.
100221: Linear Algebra I

Short Name: 
LinAlg I 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
Yes 
Together with Analysis I, this is one of the basic mathematics courses. It introduces
vector spaces and linear maps, which play an important role
throughout mathematics and its applications.The course begins by introducing the concept of a vector space
over an arbitrary field (for example, the real or complex numbers)
and the concept of linear independence, leading to the notion of
``dimension''. We proceed to define linear maps between vector
spaces and discuss properties such as nullity and rank. Linear maps
can be represented by matrices and we show how matrices can be used
to compute ranks and kernels of linear maps or to solve linear
systems of equations.
In order to study some geometric problems and talk about lengths
and angles, we introduce an additional structure called the inner
or scalar product on real vector spaces. Properties of Euclidean
vector spaces and orthogonal maps are treated, including the
CauchySchwarz inequality, GramSchmidt orthonormalization and
orthogonal and unitary groups.
An endomorphism is a linear map from a vector space to itself
and is represented by a square matrix. We study the trace and
determinant of endomorphisms and matrices and discuss eigenvalues
and eigenvectors. We discuss the question whether a matrix is
diagonalizable and state the theorem on Jordan Normal Form which
provides a classification of endomorphisms.
This course has no formal prerequisites; incoming students with
a strong mathematics background are encouraged to take this class
in their first semester. However, a familiarity with mathematical
reasoning and proof (e.g. proof by induction or by contradiction),
such as introduced in General Mathematics and
Computational Science I, is required.
100313: Real Analysis

Short Name: 
RealAna 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
Real Analysis is one of the core advanced courses in the
Mathematics curriculum. It introduces measures, integration,
elements from functional analysis, and the theory of function
spaces. Knowledge of these topics, especially Lebesgue integration,
is instrumental in many areas, in particular, for stochastic
processes, partial differential equations, applied and harmonic
analysis, and is a prerequisite for the graduate course in
Functional Analysis.The course is suitable for undergraduate students who have taken
Analysis I/II, and Linear Algebra I; it should also be taken by
incoming students of the Graduate Program in the Mathematical
Sciences. Due to the central role of integration in the applied
sciences, this course provides an excellent foundation for
mathematically advanced students from physics and engineering.
100361: Ordinary Differential Equations and Dynamical Systems

Short Name: 
DynSystems 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 and 100221 

Corequisites: 
None 

Tutorial: 
Yes 
Dynamical systems is an topic which links pure mathematics with
applications in physics, biology, electrical engineering, and
others. The course will furnish a systematic introduction to
ordinary differential equations in one and several variables,
focusing more on qualitative aspects of solutions than on explicit
solution formulas in those few cases where such exist. It will be
shown how simple differential equations can lead to complicated and
interesting, often ``chaotic'' dynamical behavior, and that such
arise naturally in the ``real world''. We will also discuss
timediscrete dynamical systems (iteration theory) with its
relations and differences to differential equations.
100362: Introductory Partial Differential Equations

Short Name: 
Intro PDE 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
This course is a rigorous, but elementary introduction to the
theory of partial differential equations: classification of PDEs,
linear prototypes (transport equation, Poisson equation, heat
equation, wave equation); functional setting, function spaces,
variational methods, weak and strong solutions; first order
nonlinear PDEs, introduction to conservation laws; exact solution
techniques, transform methods, power series solutions, asymptotics.This course alternates with Partial
Differential Equations which takes a functional analytic
approach to partial differential equations.
100472: Partial Differential Equations

Short Name: 
PDE 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100313 

Corequisites: 
None 

Tutorial: 
No 
The course is an introduction to the theory of partial differential
equations in a Sobolev space setting. Topics include Sobolev
spaces, second order elliptic equations, parabolic equations,
semigroups, and a selection of nonlinear problems.This course differs from the approach taken in Introductory Partial Differential Equations
which focuses on solutions in classical function spaces via Greens
functions. It may therefore be taken by students who have attended
Introductory Partial Differential
Equations, but we will again start from basic principles so
that Introductory Partial Differential
Equations is not a prerequisite.
100382: Stochastic Processes

Short Name: 
StochProc 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
This course is an introduction to the theory of stochastic
processes. The course will start with a brief review of probability
theory including probability spaces, random variables,
independence, conditional probability, and expectation.The main part of the course is devoted to studying important
classes of discrete and continuous time stochastic processes. In
the discrete time case, topics include sequences of independent
random variables, large deviation theory, Markov chains (in
particular random walks on graphs), branching processes, and
optimal stopping times. In the continuous time case, Poisson
processes, Wiener processes (Brownian motion) and some related
processes will be discussed.
This course alternates with Applied
Stochastic Processes.
100383: Applied Stochastic Processes

Short Name: 
ApplStochProc 

Type: 
Lecture 

Credit Points: 
7.5 

Prerequisites: 
100212 

Corequisites: 
None 

Tutorial: 
Yes 
This course aims at an introduction to the mathematical theory of
financial markets that discusses important theoretical concepts
from the theory of stochastic processes developed in parallel to
their application to the mathematical finance.The applied part of this course revolves around the central
question of option pricing in markets without arbitrage which will
be first posed and fully solved in the case of binomial model.
Interestingly enough, many of the fundamental concepts of financial
mathematics such as arbitrage, martingale measure, replication and
hedging will manifest themselves, even in this simple model. After
discussing conditional expectation and martingales, more
sophisticated models will be introduced that involve multiple
assets and several trading dates. After discussing the fundamental
theorem of asset pricing in the discrete case, the course will turn
to continuous processes. The Wiener process, Ito integrals, basic
stochastic calculus, combined with the main applied counterpart,
the BlackScholes model, will conclude the course.
This course alternates with Stochastic
Processes.
320101: General Computer Science I

Short Name: 
GenCompSci I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The course covers the fundamental concepts and techniques of
computer science in a bottomup manner. Based on clear mathematical
foundations (which are developed as needed) the course discusses
abstract and concrete notions of computing machines, information,
and algorithms, focusing on the question of representation vs.
meaning in Computer Science.
To have a theoretical notion of computation, we introduce
inductively defined structures, term representations, abstract
interpretation via equational substitution. This is contrasted with
a first concrete model of computation: Standard ML, which will also
act as the primary programming language for the course. We cover a
basic subset of ML that includes types, recursion, termination,
lists, strings, higherorder programming, effects, and exceptions.
Back on the theoretical side, we cover string codes, formal
languages, Boolean expressions (syntax) and Boolean Algebras
(semantics). The course introduces elementary complexity theory
(bigO), applying it to analyzing the gatecomplexity of Boolean
Expressions (prime implicants and Quine McCluskey's algorithm).
320102: General Computer Science II

Short Name: 
GenCS II 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
320101 

Corequisites: 
None 

Tutorial: 
No 
The course continues the introduction of the fundamental concepts
and techniques of Computer Science. Building on Boolean Algebra, it
introduces Propositional Logic as a model for general logical
systems (syntax, semantics, calculi). Based on elementary graph
theory, combinatory circuits are introduced as basic logic
computational devices. Interpreting sequences of Boolean values as
representations of numbers (in positional number systems,
twoscomplement system), Boolean circuits are extended to numerical
computational machines (presenting adders, subtracters,
multipliers) and extended to basic ALUs. The course introduces very
elementary computer architectures and assembly language concrete
computational devices, and compares them to Turing machines to
fathom the reach of computability.In a final part of the course, two topics of general Computer
Science are covered in depth, for instance ``search algorithms''
and ``programming as search'' to complement the rather horizontal
(i.e.^Mmethodsoriented) organization of the course with vertically
(i.e.^Mgoaloriented) organized topics.
Topics: Propositional logic, calculi, soundness, completeness,
automated theorem proving, combinatory circuits, assembler Turing
machines, search, logic programming.
320111: NatSciLab Unit Computer Science I

Short Name: 
NatSciLab CS I 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This lab unit is a first introduction to programming using the
programming language C. The course covers fundamental procedural
programming constructs and simple algorithms in a handson manner.
320112: NatSciLab Unit Computer Science II

Short Name: 
NatSciLab CS II 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
320111 

Corequisites: 
None 

Tutorial: 
No 
This lab unit is a continuation of the first year CS lab unit and
deepens the basic programming skills from the first lab. It covers
advanced topics of C programming such as data structures, file
handling, libraries, and debugging techniques.
320201: Fundamental Computer Science I (Algorithms and Data
Structures)

Short Name: 
Fund CS I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
320102, 120112 

Corequisites: 
None 

Tutorial: 
No 
This course introduces a basic set of data structures and
algorithms that form the basis of almost all computer programs. The
data structures and algorithms are analyzed in respect to their
computational complexity with techniques such as worst case and
amortized analysis.Topics: Fundamental data structures (lists, stacks,
trees, hash tables), fundamental algorithms (sorting, searching,
graph traversal).
300301: Dynamical Systems and Control

Short Name: 
DynSys+Control 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
Dynamical systems in nature and technology often behave in a
counterintuitive fashion and are thus difficult to predict and to
regulate. The counterintuitive behavior comes from nonlinear
interactions between the system components and the nonlinear
processing of incoming information. This course is an introduction
to nonlinear dynamics and control with a focus on a broad range of
applications. Topics include:1. Low dimensional autonomous dynamical systems: formulation as
differential equation, flow, fixed points, stability, stability
criteria, potentials and Lyapunov functionals, simple local
bifurcations (saddlenode, pitchfork, transcritical, cusp, Hopf),
simple numerical schemes, timediscrete maps (fixed points,
stability), introduction to chaos.
2. Control for linear systems: general matrixbased solution for
driven linear ODEs, reachability, controllability, observability,
Grammatrix for determining control laws and for reconstruction,
linearstate feedback controller, stablestate estimation,
introduction to optimal control.
3. Reactiondiffusion partial differential equations
(activatorinhibitor, relations to control), stability of
stationary solutions, Turing instability.
320322: Graphics and Visualization

Short Name: 
CSGV 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
Please check on Campus Net 

Corequisites: 
Please check on Campus Net 

Tutorial: 
No 
Course topics: input and output devices, 2D and 3D graphic
algorithms, transformations, projections, hidden line/surface
removal, shading algorithms, color reduction. Role of the course in
the curriculum: This course introduces the basic algorithms and
techniques in computer graphics and data visualization. Students
taking this course will develop an understanding how computer
graphics are created and which algorithms are implemented by
graphic processors. This course is recommended for all EECS
students with an interest in data visualization and computer
graphics.
300491: Convex Optimization

Short Name: 
ConOpt 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
Please Check Campus Net 

Corequisites: 
Please Check Campus Net 

Tutorial: 
No 
Convex optimization is an important part of optimization in
general. It deals with convex functions on convex domains. Convex
problems are more general than linear ones but although convex
optimization is about nonlinear problems, optimum solutions are
still globally optimal. The course is an introduction to the theory
and application of convex optimization. It provides a wide variety
of examples and discusses different optimization algorithms.
300493: Optimization Lab

Short Name: 
OptLab 

Type: 
Lab 

Credit Points: 
5 

Prerequisites: 
Please Check Campus Net 

Corequisites: 
Please Check Campus Net 

Tutorial: 
No 
This is a handson extension to the optimization lecture. Based on
solving several optimization problems, students develop broad
practical experience concerning implementation and application of
optimization techniques. Topics covered include standard
optimization tools but also genetic algorithms and learning
algorithms. A large part of the lab focuses on algorithms for games
(like reversi).
300501: Computational Electromagnetics

Short Name: 
CompElectromagnetics 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
Please Check Campus Net 

Corequisites: 
Please Check Campus Net 

Tutorial: 
No 
Recent advances in diverse engineering and scientific disciplines,
such as optical and wireless communications, electronic computing,
medical imaging, radar, and remote sensing, have been enabled by
highfrequency electronic devices operating in the radiofrequency,
microwave, and optical regimes. Although the behavior of such
devices is completely described by Maxwell's equations, direct
analytical solutions are only possible for very simple structures.
With the advent of powerful computers, however, exact numerical
solutions of Maxwell's equations have been developed, allowing
highly accurate characterization of nearly arbitrary structures.
Inclusion of these computational electromagnetic (CEM) techniques
in powerful computer assisted design (CAD) packages allows the
engineer to test and modify potential highfrequency designs
conveniently on a computer, shortening the design cycle and saving
valuable resources.This course covers the most important developments in CEM,
allowing students to visualize the behavior of complex devices, to
understand the benefits/limitations of commercial packages, and to
develop new CEM codes when needed. Although the target application
is electromagnetics, the same methods for obtaining numerical
solutions to partial differential equations can be applied to
general problems in physics and engineering. This lecture stresses
an analytical treatment of the various CEM techniques, where the
lecture is complimented by a number of short assignments requiring
derivations or closedform analysis. Students interested in gaining
practical experience writing and applying CEM codes are encouraged
to take the Computational Electromagnetics Lab in parallel.
Concepts Covered:
 Basic numerical techniques: numerical integration, MonteCarlo
analysis, solutions of simultaneous equations
 Finitedifference techniques: Laplace equation, the wave
equation, finitedifference timedomain (FDTD), absorbing boundary
conditions (ABC), eigenvalue problems and mode solutions
 Methodofmoments: Green's functions, expansion and weighting
functions, surface and volume methods
 Variational methods: variational calculus, functionals,
weighted residual method
 Finiteelement method (FEM): element equations, mesh
generation, solutions
 Introduction to modern developments: multipole techniques,
raytracing, domain decomposition, hybrid methods
200101: General Physics I

Short Name: 
GenPhys I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This course is an introduction to physics and its basic principles,
covering classical mechanics and thermodynamics. It is a mandatory
course for physics majors but can also serve as a general
introduction to physics for all other majors.It is neither the traditional experimental physics lecture, nor
a pure theoretical physics course. Both aspects are combined and
special emphasis is laid on general principles, not on extensive
mathematical derivations. Nevertheless, the course teaches calculus
based physics so that some basic mathematical knowledge will be
required. Experiments are integrated into the lectures.
The course consists of the following two parts:
 Mechanics including: motion and coordinate systems; forces and
Newton laws; work and energy; collisions and momentum; rotations,
torque, angular momentum; Kepler laws and gravitation; continuum
mechanics and elasticity; fluid mechanics; harmonic oscillator,
damping, resonance; waves.
 Thermodynamics including: temperature, heat, heat capacity;
transport phenomena; ideal gas, kinetic gas theory; MB
distribution; Brownian motion; 1st law, energy, heat and work; 2nd
law, cyclic processes, engines; entropy and statistical
interpretation; thermodynamic potentials.
200102: General Physics IIA (Electromagnetism, Optics)

Short Name: 
GenPhys IIA 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
200101 

Corequisites: 
None 

Tutorial: 
No 
This course is a continuation of General Physics I (200101). It is
mandatory for physics majors, but also interesting for e.g. life
science or electrical engineering majors. It is an introduction to
physics, covering electromagnetism and optics. It is neither the
traditional experimental physics lecture, nor a pure theoretical
physics course. Both aspects are combined, special emphasis is laid
on general principles, not on mathematical derivations.
Nevertheless, the course teaches calculus based physics so that
some basic mathematical knowledge will be required. Experiments are
integrated into the lectures.The course consists of the following two parts:
 Electromagnetism: electric charge, field and potential,
capacitance and dielectrics; resistance and current; magnetic force
and field; magnetization and induction; AC/DC circuits; Maxwell
equations and electromagnetic waves.
 Optics: waves and acoustics; refractive index, reflection,
dispersion, polarization, scattering; lenses, geometrical optics,
optical instruments; interference, interferometers, diffraction,
resolving power.
200103: General Physics IIB (Modern Physics)

Short Name: 
GenPhys IIB 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
200101 

Corequisites: 
None 

Tutorial: 
No 
This course is a continuation of General Physics I (200101). It is
mandatory for physics majors but also interesting for all other
majors. It is also an introduction to physics, covering all aspects
of modern physics such as quantum physics, atomic and nuclear
physics, particle physics and relativity. It is neither the
traditional experimental physics lecture, nor a pure theoretical
physics course. Both aspects are combined, special emphasis is laid
on general principles, not on mathematical derivations.
Nevertheless, the course teaches calculus based physics and some
mathematical knowledge will be required. Experiments are integrated
into the lectures.The course introduces the following topics:
 Special relativity: Lorentz transformation, rest mass.
 Quantum physics: photons, electrons, wave nature of particles;
Schroedinger equation, Heisenberg uncertainty principle.
 Atomic physics: Xray and atomic structure, periodic systems,
spin electronic excitations, atomic spectra.
 Molecules and condensed matter: molecular bonds and vibrations;
crystals and semiconductors.
 Nuclear and particle physics: elementary particles,
accelerators and detectors, quarks, standard model, decay of
nuclei, nuclear reactions, nuclear fusion and fission.
200111: NatSciLab Unit Physics I

Short Name: 
NatSciLab Phys I 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
None 

Corequisites: 
200101 

Tutorial: 
No 
The Natural Science Laboratory Course Module in Physics forms an
integral part of firstyear physics education at Jacobs University.
The physics module occupies 8 of the 24 afternoon sessions of the
first year Natural Science Laboratory Course. For students planning
to major in the School of Engineering and Science, participation in
the Natural Science Lab Course is mandatory (lectures and lab
course modules have to correspond). For all other students wishing
to enroll in the physics lab module, attendance of the General
Physics lecture is corequisite, since the lab course is taught in
close coordination with the lecture. In the physics module,
participants carry out 7 experiments in total covering topics of
mechanics, thermodynamics and optics. Aims of the lab course are:
(1) to gain hands on experience of the material taught in General
Physics, (2) to learn how scientific experiments are planned,
carried out, analysed, and reported, (3) learn about technical
aspects of measuring and measuring devices.
200112: NatSciLab Unit Physics II

Short Name: 
NatSciLab Physics II 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
Please check on Campus Net 

Corequisites: 
Please check on Campus Net 

Tutorial: 
No 
The Natural Science Laboratory Course Module in Physics forms an
integral part of firstyear physics education at Jacobs University.
The physics unit occupies 8 of the 24 afternoon sessions of the
first year Natural Science Laboratory Course. For students planning
to major in the School of Engineering and Science, participation in
the Natural Science Lab Course in mandatory (lectures and lab
course units have to correspond). For all other students wishing to
enroll in the physics lab unit, attendance of the General Physics
lecture is a corequisite, since the lab course is taught in
coordination with the lecture. In the physics unit, participants
carry out 8 experiments in total covering topics in
Electromagnetism and Quantum Physics. Aims of the lab course are:
(1) to gain hands on experience of material taught in General
Physics, (2) to learn how scientific experiments are planned,
carried out, analyzed, and reported, (3) to learn about technical
aspects of measuring and measuring devices.
520101: General Biochemistry and Cell Biology I

Short Name: 
GenBCCB I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This is a unique course that gives, over the first year of studies
at Jacobs University, a comprehensive introduction to biochemistry
and cell biology. At the end of the course, students will have
gained knowledge of the foundations and the scope of the subject
and of the specific scientific reasoning that underlies research in
this field. Topics covered will be the biochemistry and biophysics
of DNA, proteins (especially enzymes), carbohydrates, and lipids;
the buildup and the breakdown of these substances; the (animal,
plant, and bacterial) cell, its substructure, and its organelles;
an introduction to the most common chemical reactions in living
cells and the underlying thermodynamic, chemical, and kinetic
principles, including metabolism and its regulation; and
introductory overviews of specialized fields such as biophysics,
structural biology, molecular machines, molecular neurobiology,
immunology, molecular genetics, developmental biology, and cancer.
Information about the techniques and strategies to obtain knowledge
and to ask questions in molecular life science, as well as
historical outlines, will accompany each topic.This course requires solid High School knowledge of both biology
and chemistry, or the willingness to acquire it at Jacobs
University. Depending on their previous training, prospective
Biochemistry and Cell Biology major students are advised to take
General Chemistry or General Biology or both in addition to this
course. General Biochemical Engineering is also a very useful
complement.
520102: General Biochemistry and Cell Biology II

Short Name: 
GenBCCB II 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
520101 

Corequisites: 
None 

Tutorial: 
No 
This is the second part of the comprehensive introduction to
biochemistry and cell biology with special emphasis on the
connections to the related fields chemistry and biology. In the
spring semester, the emphasis of the course will be on more complex
cell biological topics, such as the synthesis, topogenesis and
breakdown of cellular components in the context of the cellular
environment, and introductory overviews of specialized fields such
as biophysics, structural biology, cell cycle, molecular
neurobiology, immunology, DNA technology, developmental biology,
and cancer. Information about the techniques and strategies to
obtain knowledge and to ask questions in molecular life science, as
well as historical outlines, will accompany each topic.Good High School knowledge of both biology and chemistry, or the
willingness to acquire it in selfstudy, is assumed. Prospective
Biochemistry and Cell Biology major students are advised to take
General Chemistry or General Biology or both in addition to this
course.
520111: NatSciLab Unit Biochemistry and Cell Biology I

Short Name: 
NatSciLab BCCB I 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
Please check on Campus Net 

Corequisites: 
Please check on Campus Net 

Tutorial: 
No 
This course trains basic laboratory skills and gives an
introduction to biochemical and cell biological work in the
laboratory. The course parallels the general biochemistry and cell
biology lecture. An introduction is given to substance classes on
one hand and methods on the other. Course days include e.g., the
handling of glass and micropipettes, balances, spectrophotometers
and light microscopes. Experiments include gel filtration, thin
layer chromatography of plant pigments, titration, pHdependence of
enzymes, identification of carbohydrates, microscopy of sperms and
muscle etc. For each course day, a lab report is handed in.
520112: NatSciLab Unit Biochemistry and Cell Biology II

Short Name: 
NatSciLab BCCB II 

Type: 
Lab 

Credit Points: 
2.5 

Prerequisites: 
Please check on Campus Net 

Corequisites: 
Please check on Campus Net 

Tutorial: 
No 
This course trains basic laboratory skills and gives an
introduction to biochemical and cell biological work in the
laboratory. The course parallels the general biochemistry and cell
biology lecture. An introduction is given to substance classes on
one hand and methods on the other.As a continuation of the fall course, this time the focus lies
on DNA and RNA. Course days include e.g., the handling of glass and
micropipettes, balances, spectrophotometers and light microscopes.
Experiments include isolation of DNA and RNA from pea seedlings,
isolation of DNA from stressed C6 glioma cells and detection of an
apoptotic DNA ladder by means of agarose gel electrophoresis,
sterile cultivation of yeast cells, determination of cytotoxicity,
fixation, staining and microscopic investigation of M phase
cells.
520201: Advanced Biochemistry and Molecular Biology I

Short Name: 
AdvBCMB I 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
520101, 520102 

Corequisites: 
None 

Tutorial: 
No 
The course intends to give a detailed understanding of the chemical
reactions that underlie life. In the first part the structures,
dynamics and chemistry of important biomolecules will be described.
The thermodynamics and kinetics of ligand binding to proteins and
enzyme catalysis will be explained and enzymatic catalysis explored
at the molecular and atomic level. The second part focuses on
metabolism and describes how energy is produced by living organisms
and how the molecules of life are synthesised and degraded. A
special focus will be set on common principles and the integration
of the metabolism. The third part of the course explains how the
genetic information stored in the DNA sequence is maintained and
expressed. In addition the mechanism of DNA binding and
modification by proteins and enzymes will be presented. The
techniques of modern molecular biology will be described and the
results of the human genome project discussed.
520202: Advanced Biochemistry and Molecular Biology II

Short Name: 
AdvBCMB II 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
520201 

Corequisites: 
None 

Tutorial: 
No 
The course intends to give a detailed understanding of the chemical
reactions that underlie life. In the first part the structures,
dynamics and chemistry of important biomolecules will be described.
The thermodynamics and kinetics of ligand binding to proteins and
enzyme catalysis will be explained and enzymatic catalysis explored
at the molecular and atomic level. The second part focuses on
metabolism and describes how energy is produced by living organisms
and how the molecules of life are synthesised and degraded. A
special focus will be set on common principles and the integration
of the metabolism. The third part of the course explains how the
genetic information stored in the DNA sequence is maintained and
expressed. In addition the mechanism of DNA binding and
modification by proteins and enzymes will be presented. The
techniques of modern molecular biology will be described and the
results of the human genome project discussed.
550201: Bioinformatics and Computational Biology

Short Name: 
BICB 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
Intention of the course is to provide an overview over current
bioinformatic approaches in sequence and genome analysis by a
direct linkage of the biological problem with the computational
approaches to solve them. An important task of bioinformatics is to
put data into a larger context by identifying statistical
similarities between the data. Finding genes in DNA sequences,
identifying common structural features in (evolutionarily) related
protein sequences and extracting functional properties from the
architecture of cellular networks are examples of this general
strategy of bioinformatics, which will be highlighted throughout
the course.Major topics will be: Biological data, acquisition and storage,
Similarity and Alignment, Pairwise and Multiple Sequence
comparisons, Sequence patterns and motifs, Genome analysis, Genome
signatures, Hidden Markov models, Networks.
550221: Bioinformatics Lab Course

Short Name: 
BICB Lab 

Type: 
Lab 

Credit Points: 
7.5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
Part 1: Sequence analysis. The increasing amount of biological data in particular of protein
and DNA sequences requires students to gain practical experience
with algorithms and computational tools for sequence analysis. The
lab course intends to train students in the application of
computational approaches to nucleic acid and protein sequence
analysis and secondary structure prediction. The script language
Perl will be introduced and students will be trained to write own
applications and to program bioinformatics algorithms and methods.
It will give a practical introduction and an overview on available
and frequently applied sequence analysis methods including the
software to align and compare sequences and to predict coding and
noncoding regions. Multiple alignment methods will be used to
align several sequences and to identify conserved elements. In
addition, computational methods for the prediction of protein and
RNA secondary structure will be introduced and applied to selected
examples. The generation of a SQL data base in combination with a
script language will be introduced.Part 2: Genomics. In the second part of the lab course
(Genomics) hands on in genome analysis will be obtained. Starting
with a students seminar on a special topic related to genome
research every week, several tools will be tested and used for
sequence analysis. We will use webbases systems as well as
standalone tools installed on a local server. Complete genomes as
well as genomic fragments will be analysed. The genomic fragments
will serve as the template for in depth annotation using an open
source annotation system (GenDB). A local installation of the
phylogenetic software package ARB will be used for phylogenetic
inference and tree reconstruction of selected functional genes.
550321: Computational Systems Biology

Short Name: 
SysBio 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
Systems Biology aims at understanding the functioning of a cell due
to the concerted action of its constituents. At the same time,
however, many spatial and temporal scales contribute to cellular
organization and turn it into a complex interplay of regulatory
processes. It seems, therefore, futile addressing this problem of
system understanding without the appropriate toolbox. This course
provides the toolbox for ''doing Systems Biology''.In the first part we will look at general principles of systems
modeling. We will discuss elementary models of dynamic processes,
which help understand large classes of biological systems and then
see, how an increase in complexity changes properties of the
model.
The second part covers system biologic models of specific
cellular processes: metabolism, signal transduction and gene
regulation.
In the last segment we will discuss the integration of
theoretical approaches: How can data sets on different levels of a
biological systems been related and put into a wider context? How
can specific models be interlinked, in order to pass from a minimal
model (the domain of theoretical biology) to a realistic
``insilico'' description (the realm of systems biology)?
We also look at the current computational infrastructure of
systems biology: data bases for mathematical models, standards for
data formats. Beyond ACM, this course may also be of interest to
biochemistry and computer science majors.
560302: Design of Biological Molecules and Systems

Short Name: 
DBMS 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The course is intended to give an overview on
theoretical/computational as well as experimental aspects of
biological molecule and system design. It will include state of the
art genetic engineering methods for directed protein evolution,
molecular breeding and practical approached of rational,
combinatorial and evolutionary molecule design. On the theoretical
side the course covers computational approaches of biomolecular
modelling, structure prediction and mathematical models of
evolution. This includes energetic and entropic contributions to
ligandreceptor interactions and biomolecule stability. It covers
also the prediction of the effect of mutations on biomolecular
structure and function, prediction of ligandreceptor binding and
an introduction to computational drug design and virtual screening
methods.
032101: Microeconomics

Short Name: 
Microeconomics 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
The study of economics concerns itself with the allocation of
scarce resources and the associated implications for efficiency,
equity, and human welfare. This course introduces the field of
microeconomics, focusing specifically on the role of markets in
facilitating exchange between the individual households, firms, and
government institutions that make up the economy. Topics addressed
include consumer theory, the behavior of firms, competition, and
monopoly. The course applies the theoretical concepts covered to
contemporary policy questions, such as when government intervention
is justified to correct market imperfections.Students who successfully complete this course will not receive
credits towards the 180 ECTScredits required for their BA degree
from the course Introduction to
Economics. These courses are mutually exclusive due to
comparable content.
032102: Macroeconomics

Short Name: 
Macroeconomics 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This course provides an introduction to the analysis of aggregate
output, employment and economic growth and their relationship to
the policy issues of unemployment, inflation and the balance of
payments. Other topics include: national accounting; aggregate
income and expenditure analysis; macroeconomic models of income
determination; consumption, saving and investment functions; the
role of money and banks; interactions between goods and money
markets in equilibrium and disequilibrium situations.Students who successfully complete this course will not receive
credits towards the 180 ECTScredits required for their BA degree
from the course Introduction to
Economics. These courses are mutually exclusive due to
comparable content.
930201: Introduction to Economics

Short Name: 
Intro.Economics 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This lecture introduces students to the institution of the market.
It reconstructs the micrologic of market exchanges at the level of
individual market participants (microeconomics), analyzes the
resulting macropatterns at the level of market aggregates
(macroeconomics), and looks into the role that governments play in
defining, shaping, and destroying market relations.Students who successfully complete this course will not receive
credits towards the 180 ECTScredits required for their BA degree
from the courses Microeconomics and
Macroeconomics. These courses are
mutually exclusive due to comparable content.
930221: Managerial & Financial Accounting

Short Name: 
Accounting MLM 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
no 
Physical movements of goods leave a financial trail. Accounting is
the art of capturing this trail and transforming it into meaningful
information for management and other stakeholders. This course
provides an introduction to accounting principles. It focuses on
measuring the financial position and performance of a firm, on
reporting cash flows and on analyzing financial 20statements. It
consists of modules on strategic and operative planning as well as
on controlling (target setting, feedback and feedforward control,
balanced scorecard). Cost allocation, full costing and
costvolumeprofit analysis are the focus of managerial accounting.
930241: Finance

Short Name: 
Finance 

Type: 
Seminar 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
Corporate Finance is crucial to the growth of all firms. This is
even more so in a global environment that is characterized by
liquidity shortages and turmoil in capital markets.This course will provide students with the basics of corporate
finance. It will introduce to the analytical tools and the
necessary techniques for the financial management of a firm.
Students will discuss these techniques in various contexts: the
modern theories of corporate finance, corporate governance, value
and capital budgeting, risk and return, capital structure and
dividend policy, finance decisions as well as long term and short
term financial planning.
930312: Firms and Markets

Short Name: 
Firms/Markets 

Type: 
Seminar 

Credit Points: 
5 

Prerequisites: 
Please check on Campus Net 

Corequisites: 
Please check on Campus Net 

Tutorial: 
No 
This seminar continues the analysis of the market. It asks why in
market economies, not all economic transactions take place within
the market. Why are some transactions moved outside of the market
and coordinated hierarchically within business firms? The seminar
examines both the internal organization and management of business
firms and their external behavior. The topics covered include the
economics of transaction costs, agency theory, elementary game
theory, competitive advantage, strategy formation, and strategic
pricing.
990121: Statistical Concepts and Data Analysis

Short Name: 
Stats_Concepts 

Type: 
Lecture/Lab 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
No 
This course aims to provide an introduction to fundamental
statistical concepts and tools for data analysis. It is intended as
a onesemester course that combines selected topics from both the
mandatory statistical methods courses for SHSS students
(Statistical Methods I: Exploring Relationships and Comparing
Groups and Statistical Methods II: Classification, Modelling and
Prediction) to offer the more relevant topics for logistics and SES
major students. The course will focus on the understanding of
statistical concepts and the application of statistical techniques.
While some formulae might be used, no stringent mathematical
derivation will be provided. The general objective is to become an
intelligent user of the various univariate and multivariate
statistical techniques and to acquire the knowledge for deciding
which procedure is most suitable for the given business situation.
We will discuss the theoretical aspects of the statistical methods,
discuss the assumptions for their use, reflect on their limitations
and the controversies they create. In practical sessions we will
learn how to run the particular procedures using SPSS and/or R, how
to interpret the computer output and how to skillfully communicate
the results of statistical analyses.
990222: Econometrics

Short Name: 
Econometrics 

Type: 
Lecture 

Credit Points: 
5 

Prerequisites: 
None 

Corequisites: 
None 

Tutorial: 
no 
This course focuses on the analysis of secondary data in the
business world. Thus, one focus of the course consists of
quantitative methods used in economy and business. We will expand
on the knowledge acquired in the statistics class and intensify
discussion of multiple regression analysis, in particular with an
emphasis on longitudinal/time dependent data. The second focus of
the course is on the analysis of large data sets that are created
during the regular business process, such as billing data, customer
information, etc.; data, that is more and more analysed by
computerintensive methods to find structures and patterns.The general objective is to become familiar with classic and
contemporary methods that are used in econometric and business
analyses and to become a critical reader of case studies in this
field. We will take a practical approach to learn how to run the
particular procedures in state of the art software. To foster the
practical approach homework and projects will be assigned. By the
end of this course, students will know the rules for being
competent practitioners of econometrics. This involves:
Understanding how data should be organized for undertaking
econometric modeling and the steps required for preparing data for
analysis; Recognizing what technique to select from the econometric
toolkit given the pattern of values in the data; Being able to
interpret results with respect to both their statistical and
economic/social significance; Be able to cast a skeptical eye on
econometric results in the literature; Have fun working with social
science data.
