Search result: Catalogue data in Autumn Semester 2016

Computer Science Bachelor Information
Bachelor Studies (Programme Regulations 2016)
First Year Examinations
First Year Examination Block 1
NumberTitleTypeECTSHoursLecturers
401-0131-00LLinear AlgebraO7 credits4V + 2UÖ. Imamoglu, O. Sorkine Hornung
AbstractApplication oriented introduction to linear algebra (vector spaces, linear transformations, matrices) , matrix decompositions (LU, QR, eigenvalue, and singular value decomposition). Introduction to the programming environment Matlab.
ObjectiveDie Lernziele sind:
- die fundamentalen Konzepte der linearen Algebra gut zu verstehen
- in der Lage zu sein, mit Hilfe von Matlab Rechenaufgaben zu lösen
- Anwendungen der linearen Algebra in der Informatik kennenzulernen
ContentLinear Algebra:
Linear systems of equations, vectors and matrices, norms and scalar products, LU decomposition, vector spaces and linear transformations, least squares problems, QR decomposition, determinants, eigenvalues and eigenvectors, singular value decomposition, applications.
Lecture notesLecture notes "Linear Algebra" (Gutknecht) in German, with English expressions for all technical terms.
Prerequisites / NoticeThe relevant high school material is reviewed briefly at the beginning.
252-0025-00LDiscrete MathematicsO7 credits4V + 2UU. Maurer
AbstractContent: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi).
ObjectiveThe primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory.
ContentSee course description.
Lecture notesavailable (in english)
252-0026-00LAlgorithms and Data Structures Information O7 credits3V + 2U + 1AP. Widmayer, M. Püschel
AbstractThis course is about fundamental algorithm design paradigms, classic algorithmic problems, and data structures. The connection between algorithms and data structures is explained for geometric and graph problems. For this purpose, fundamental graph theoretic concepts are introduced.
ObjectiveAn understanding of the design and analysis of fundamental algorithms and data structures.
ContentEs werden grundlegende Algorithmen und Datenstrukturen vorgestellt und analysiert. Dazu gehören auf der einen Seite Entwurfsmuster für Algorithmen, wie Induktion, divide-and-conquer, backtracking und dynamische Optimierung, ebenso wie klassische algorithmische Probleme, wie Suchen und Sortieren. Auf der anderen Seite werden Datenstrukturen für verschiedene Zwecke behandelt, darunter verkettete Listen, Hashtabellen, balancierte Suchbäume, verschiedene heaps und union-find-Strukturen. Weiterhin wird Adaptivität bei Datenstrukturen (wie etwa Splay-Bäume) und bei Algorithmen (wie etwa online-Algorithmen) beleuchtet. Das Zusammenspiel von Algorithmen und Datenstrukturen wird anhand von Geometrie- und Graphenproblemen illustriert. Hierfür werden grundlegende Konzepte der Graphentheorie eingeführt.
LiteratureTh. Ottmann, P.Widmayer: Algorithmen und Datenstrukturen, Spektrum-Verlag, 5. Auflage, Heidelberg, Berlin, Oxford, 2011
252-0027-00LIntroduction to Programming Information O7 credits4V + 2UT. Gross
AbstractIntroduction to fundamental concepts of modern programming and operational skills for developing high-quality programs, including large programs as in industry. The course introduces software engineering principles with an object-oriented approach based.
ObjectiveMany people can write programs. The "Introduction to Programming" course goes beyond that basic goal: it teaches the fundamental concepts and skills necessary to perform programming at a professional level. As a result of successfully completing the course, students master the fundamental control structures, data structures, reasoning patterns and programming language mechanisms characterizing modern programming, as well as the fundamental rules of producing high-quality software. They have the necessary programming background for later courses introducing programming skills in specialized application areas.
ContentBasics of object-oriented programming. Objects and classes. Pre- and postconditions, class invariants, Design by Contract. Fundamental control structures. Assignment and References. Basic hardware concepts. Fundamental data structures and algorithms. Recursion. Inheritance and interfaces, introduction to event-driven design and concurrent programming. Basic concepts of Software Engineering such as the software process, specification and documentation, reuse and quality assurance.
Lecture notesThe lecture slides are available for download on the course page.
LiteratureSee the course page for up-to-date information.
Prerequisites / NoticeThere are no special prerequisites. Students are expected to enroll in the other courses offered to first-year students of computer science.
First Year Examination Block 2
Offered in the spring semester.
NumberTitleTypeECTSHoursLecturers
401-0211-00LAnalysis I
Does not take place this semester.
The course unit will be offered again in the spring semester 2017.
O7 credits4V + 2Unot available
AbstractReal and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable, introduction to ordinary differential equations
ObjectiveReal and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable, introduction to ordinary differential equations
ContentReal and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable, introduction to ordinary differential equations
Lecture notesMichael Struwe. _Analysis für Informatik._ ETH Zürich, 2010.
LiteratureJürgen Pöschel. _Etwas Analysis._ Springer Spektrum, 2014.

Christian Blatter. _Ingenieur-Analysis._ 2002.
Bachelor Studies (Programme Regulations 2008)
3. Semester
Compulsory Courses (3. Sem.)
NumberTitleTypeECTSHoursLecturers
252-0057-00LTheoretical Computer Science Information O8 credits4V + 2U + 1AJ. Hromkovic
AbstractConcepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars?
ObjectiveLearning the basic concepts of computer science along their historical development
ContentThis lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems.

The main topics of the lecture are:

- alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks
- finite automata, regular and context-free grammars
- Turing machines and computability
- complexity theory and NP-completeness
- design of algorithms for hard problems
Lecture notesThe lecture is covered in detail by the textbook "Theoretical Computer Science".
LiteratureBasic literature:

1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014.

2. J. Hromkovic: Theoretical Computer Science. Springer 2004.

Further reading:

3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997
4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006.
5. I. Wegener: Theoretische Informatik. Teubner.

More exercises and examples in:

6. A. Asteroth, Ch. Baier: Theoretische Informatik
Prerequisites / NoticeDuring the semester, two non-obligatory test exams will be offered.
252-0061-00LSystems Programming and Computer Architecture Information O8 credits4V + 2U + 1AT. Roscoe
AbstractIntroduction to computer architecture and system programming:

Instruction sets, storage hiearchies, runtime structures with an
emphasis on computers as engines for the execution of compiled
programs. Interaction between system software and the hardware.
Problems that arise from the final respresentation, performance
measurement and tuning, and program portability issues are covered.
ObjectiveThe objective is to allow students to understand all aspects of the
execution of compiled (C) programs on modern architectures -- the
instruction set, the storage resources (registers, stack, memory),
input/output, the impact of compiler decisions, and the interaction
between the operating system and hardware. Two main themes are
correctness issues (esp. those that arise from the finite
representation of data) and performance issues (incl. measurement and
tuning issues). The interface to the operating system is discussed to
prepare for subsequent classes on more advanced systems topics.

The two key goals are:

1) To equip students with a thorough understanding of how to
write correct programs that run fast on modern computer, and
2) How to write correct and efficient low-level systems code.

This course does not cover how to design or build a processor or
computer.
ContentThis course provides an overview of "computers" as a
platform for the execution of (compiled) computer programs. This
course provides a programmer's view of how computer systems execute
programs, store information, and communicate. The course introduces
the major computer architecture structures that have direct influence
on the execution of programs (processors with registers, caches, other
levels of the memory hierarchy, supervisor/kernel mode, and I/O
structures) and covers implementation and representation issues only
to the extend that they are necessary to understand the structure and
operation of a computer system.

The course attempts to expose students to the practical issues that
affect performance, portability, security, robustness, and
extensibility. This course provides a foundation for subsequent
courses on operating systems, networks, compilers and many other
courses that require an understanding of the system-level
issues. Topics covered include: machine-level code and its generation
by optimizing compilers, address translation, input and output,
trap/event handlers, performance evaluation and optimization (with a
focus on the practical aspects of data collection and analysis).
LiteratureThe course is based in part on "Computer Systems: A Programmer's Perspective" (2nd Edition) by R. Bryant and D. O'Hallaron, with some additional material.
Prerequisites / Notice252-0024-00L Parallel Programming,
252-0014-00L Digital Circuits
401-0613-00LProbability and Statistics Information O6 credits3V + 2UJ. Teichmann
AbstractBasic concepts from probability and statistics:
- introduction to probability theory
- short introduction to basic concepts and methods from statistics
Objectivea) ability to understand the covered methods from probability theory and to apply them in other contexts

b) probabilistic thinking and stochastic modelling

c) ability to perform basic statistical tests and to interpret the results
ContentBasic concepts from probability and statistics with special emphasis on the topics needed in computer science

The conceptual goals are

- the laws of randomness and probabilistic thinking (thinking in probabilities)
- understanding and intuition for stochastic modelling
- simple and basic methods from statistics

The contents of the course encompasses

- an introduction to probability theory: basic concepts (probability space, probability measure), independence, random variables, discrete and continuous distributions, conditional probability, expectation and variance, limit theorems

- methods from statistics: parameter estimation, maximum likelihood and moment methods, tests, confidence intervals
Lecture notesLecture notes for the course (in German) will be made available electronically at the beginning of the course.
401-0663-00LNumerical Methods for CSE Information O7 credits4V + 2UR. Hiptmair
AbstractThe course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++.
Objective* Knowledge of the fundamental algorithms in numerical mathematics
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently
Content1. Direct Methods for linear systems of equations
2. Least Squares Techniques
3. Data Interpolation and Fitting
4. Filtering Algorithms
8. Approximation of Functions
9. Numerical Quadrature
10. Iterative Methods for non-linear systems of equations
11. Single Step Methods for ODEs
12. Stiff Integrators
Lecture notesLecture materials (PDF documents and codes) will be made available to participants:

Lecture document: Link

Lecture Git repository: Link

Tablet classroom notes: Link

Lecture recording: Link

Homework problems: Link
LiteratureU. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011.

A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000.

W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.

M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002

P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
Prerequisites / NoticeThe course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves.
Compensatory Courses
Compulsory major courses count as compensatory courses.
Major
Compulsory Major Courses
Major in Computer and Software Engineering
NumberTitleTypeECTSHoursLecturers
252-0210-00LCompiler Design
Does not take place this semester.
The course unit will be offered again in the spring semester 2017.
O8 credits4V + 3UT. Gross
AbstractThis course uses compilers as example to expose modern software development techniques.
Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: conditionals, loops, procedure calls, simple register allocation techniques.
ObjectiveLearn principles of compiler design, gain practical experience designing and implementing a medium-scale software system.
ContentThis course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project.
Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling.
LiteratureAho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition)

Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997
Prerequisites / NoticePrerequisites:
Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level.
252-0213-00LDistributed Systems Information O8 credits6G + 1AF. Mattern, R. Wattenhofer
AbstractDistributed control algorithms (mutual exclusion, logical clocks), communication models (RPC, synchronous/asynchronous communication, broadcast, events, tupel spaces), middleware, service- and resource-oriented architectures (SOAP, REST), security, fault-tolerance (failure models, consensus), replication (primary copy, 2PC, 3PC, Paxos, quorum systems), shared memory (spin locks, concurrency).
ObjectiveBecome acquainted with pertinent technologies and architectures of distributed systems.
ContentWe present the characteristics and concepts of distributed systems, and discuss distributed control algorithms (flooding, mutual exclusion, logical clocks), communications models (remote procedure call, client-server models, synchronous and asynchronous communication), abstract communication principles (broadcast, events, tupel spaces), name services, communication middleware for open systems (e.g., REST, SOAP), infrastructure for ad hoc networking (JINI), cloud computing, and mechanisms for security and safety. Having a distributed system may permit getting away with failures and malfunctions of parts of the system. We discuss fault-tolerance issues (models, consensus, agreement) as well as replication issues (primary copy, 2PC, 3PC, Paxos, quorum systems, distributed storage) and problems with asynchronous multiprocessing (shared memory, spin locks, concurrency). To get familiar with message passing communication, some of the exercises will be devoted to a practical lab where participants will develop software for a mobile platform (smartphones).
Major in Computational Science
The lecture 151-0107-20L High Performance Computing for Science and Engineering I in the autumn semester can only together with the lecture 401-0686-10L High Performance Computing for Science and Engineering II in the spring semester be accredited as compulsory course.
NumberTitleTypeECTSHoursLecturers
252-0206-00LVisual Computing Information O8 credits4V + 3UM. Gross, O. Hilliges
AbstractThis course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow.
ObjectiveThis course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.
ContentCourse topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression.

In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms.
Lecture notesA scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks.
LiteratureMarkus Gross: Computer Graphics, scriptum, 1994-2005
151-0107-20LHigh Performance Computing for Science and Engineering (HPCSE) IW4 credits4GM. Troyer, P. Chatzidoukas
AbstractThis course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering.
ObjectiveIntroduction to HPC for scientists and engineers
Fundamental of:
1. Parallel Computing Architectures
2. MultiCores
3. ManyCores
ContentProgramming models and languages:
1. C++ threading (2 weeks)
2. OpenMP (4 weeks)
3. MPI (5 weeks)

Computers and methods:
1. Hardware and architectures
2. Libraries
3. Particles: N-body solvers
4. Fields: PDEs
5. Stochastics: Monte Carlo
Lecture notesLink
Class notes, handouts
Major in Theoretical Computer Science
NumberTitleTypeECTSHoursLecturers
252-0209-00LAlgorithms, Probability, and Computing Information O8 credits4V + 2U + 1AE. Welzl, M. Ghaffari, A. Steger, P. Widmayer
AbstractAdvanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
ObjectiveStudying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.
Lecture notesWill be handed out.
LiteratureIntroduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
Electives
Compulsory major courses may also qualify as electives. Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.
NumberTitleTypeECTSHoursLecturers
252-3110-00LHuman Computer Interaction Information W4 credits2V + 1UO. Hilliges, M. Norrie
AbstractThe course provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies.
ObjectiveThe goal of the course is that students should understand the principles of user-centred design and be able to apply these in practice.
ContentThe course will introduce students to various methods of analysing the user experience, showing how these can be used at different stages of system development from requirements analysis through to usability testing. Students will get experience of designing and carrying out user studies as well as analysing results. The course will also cover the basic principles of interaction design. Practical exercises related to touch and gesture-based interaction will be used to reinforce the concepts introduced in the lecture. To get students to further think beyond traditional system design, we will discuss issues related to ambient information and awareness.
151-0107-20LHigh Performance Computing for Science and Engineering (HPCSE) IW4 credits4GM. Troyer, P. Chatzidoukas
AbstractThis course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering.
ObjectiveIntroduction to HPC for scientists and engineers
Fundamental of:
1. Parallel Computing Architectures
2. MultiCores
3. ManyCores
ContentProgramming models and languages:
1. C++ threading (2 weeks)
2. OpenMP (4 weeks)
3. MPI (5 weeks)

Computers and methods:
1. Hardware and architectures
2. Libraries
3. Particles: N-body solvers
4. Fields: PDEs
5. Stochastics: Monte Carlo
Lecture notesLink
Class notes, handouts
227-0627-00LApplied Computer ArchitectureW6 credits4GA. Gunzinger
AbstractThis lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs.
ObjectiveUnderstand the function, the design and the performance modeling of parallel computer systems.
ContentThe lecture "Applied Computer Architecture" gives technical and corporate insights in the innovative Computer Systems/Architectures (CPU, GPU, FPGA, special processors) and their real implementations and applications. Often the designs have to deal with technical limits.
Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)?
Which architecture is behind the alarm center of the Swiss Railway (SBB)?
Which computer architectures are applied for driver assistance systems?
Which computer architecture is hidden behind a professional digital audio mixing desk?
How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time?
Can the weather forecast also be processed with GPUs?
How can a good computer architecture be found?
Which are the driving factors in succesful computer architecture design?
Lecture notesScript and exercices sheets.
Prerequisites / NoticePrerequisites:
Basics of computer architecture.
227-0945-00LCell and Molecular Biology for Engineers I
This course is part I of a two-semester course.
W3 credits3GC. Frei
AbstractThe course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology.
ObjectiveAfter completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested.
ContentLectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells.

In addition, three journal clubs will be held, where one/two publictions will be discussed (part I: 1 Journal club, part II: 2 Journal Clubs). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 25% for the final grade.
Lecture notesScripts of all lectures will be available.
Literature"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter.
227-1037-00LIntroduction to Neuroinformatics Information W6 credits2V + 1UK. A. Martin, M. Cook, V. Mante, M. Pfeiffer
AbstractThe course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented.
ObjectiveUnderstanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions.
ContentThis course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks.
252-4101-00LACM-Lab
Does not take place this semester.
W4 credits3PA. Steger
AbstractSolve programming problems from previous ACM Programming Contests (see Link); learn and use efficient programming methods and algorithms.
ObjectiveThe objective of this course is to learn how to solve algorithmic problems given as descriptions in natural language, similar to those posed in ACM Programming Contests. This includes appropriate problem modeling, choice of suitable (combinatorial) algorithms, and their efficient implementation using C/C++ and the STL.
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