Search result: Catalogue data in Spring Semester 2021

Data Science Master Information
Core Courses
Core Electives
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website:
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
227-0150-00LSystems-on-Chip for Data Analytics and Machine Learning
Previously "Energy-Efficient Parallel Computing Systems for Data Analytics"
W6 credits4GL. Benini
AbstractSystems-on-chip architecture and related design issues with a focus on machine learning and data analytics applications. It will cover multi-cores, many-cores, vector engines, GP-GPUs, application-specific processors and heterogeneous compute accelerators. Special emphasis given to energy-efficiency issues and hardware-software techniques for power and energy minimization.
ObjectiveGive in-depth understanding of the links and dependencies between architectures and their energy-efficient implementation and to get a comprehensive exposure to state-of-the-art systems-on-chip platforms for machine learning and data analytics. Practical experience will also be gained through practical exercises and mini-projects (hardware and software) assigned on specific topics.
ContentThe course will cover advanced system-on-chip architectures, with an in-depth view on design challenges related to advanced silicon technology and state-of-the-art system integration options (nanometer silicon technology, novel storage devices, three-dimensional integration, advanced system packaging). The emphasis will be on programmable parallel architectures with application focus on machine learning and data analytics. The main SoC architectural families will be covered: namely, multi and many- cores, GPUs, vector accelerators, application-specific processors, heterogeneous platforms. The course will cover the complex design choices required to achieve scalability and energy proportionality. The course will will also delve into system design, touching on hardware-software tradeoffs and full-system analysis and optimization taking into account non-functional constraints and quality metrics, such as power consumption, thermal dissipation, reliability and variability. The application focus will be on machine learning both in the cloud and at the edges (near-sensor analytics).
Lecture notesSlides will be provided to accompany lectures. Pointers to scientific literature will be given. Exercise scripts and tutorials will be provided.
LiteratureJohn L. Hennessy, David A. Patterson, Computer Architecture: A Quantitative Approach (The Morgan Kaufmann Series in Computer Architecture and Design) 6th Edition, 2017.
Prerequisites / NoticeKnowledge of digital design at the level of "Design of Digital Circuits SS12" is required.

Knowledge of basic VLSI design at the level of "VLSI I: Architectures of VLSI Circuits" is required
227-0155-00LMachine Learning on Microcontrollers Restricted registration - show details
Number of participants limited to 40.
Registration in this class requires the permission of the instructors.
W6 credits3GM. Magno, L. Benini
AbstractMachine Learning (ML) and artificial intelligence are pervading the digital society. Today, even low power embedded systems are incorporating ML, becoming increasingly “smart”. This lecture gives an overview of ML methods and algorithms to process and extracts useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers (ARM-Cortex-M; RISC-V).
ObjectiveLearn how to Process data from sensors and how to extract useful information with low power microprocessors using ML techniques. We will analyze data coming from real low-power sensors (accelerometers, microphones, ExG bio-signals, cameras…). The main objective is to study in detail how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers becoming Tiny Machine learning algorithms.
ContentThe final goal of the course is a deep understanding of machine learning and its practical implementation on single- and multi-core microcontrollers, coupled with performance and energy efficiency analysis and optimization. The main topics of the course include:

- Sensors and sensor data acquisition with low power embedded systems

- Machine Learning: Overview of supervised and unsupervised learning and in particular supervised learning ( Decision Trees, Random, Support Vector Machines, Artificial Neural Networks, Deep Learning, and Convolutional Networks)

- Low-power embedded systems and their architecture. Low Power microcontrollers (ARM-Cortex M) and RISC-V-based Parallel Ultra Low Power (PULP) systems-on-chip.

- Low power smart sensor system design: hardware-software tradeoffs, analysis, and optimization. Implementation and performance evaluation of ML in battery-operated embedded systems.

The laboratory exercised will show how to address concrete design problems, like motion, gesture recognition, emotion detection, image, and sound classification, using real sensors data and real MCU boards.

Presentations from Ph.D. students and the visit to the Digital Circuits and Systems Group will introduce current research topics and international research projects.
Lecture notesScript and exercise sheets. Books will be suggested during the course.
Prerequisites / NoticePrerequisites: Good experience in C language programming. Microprocessors and computer architecture. Basics of Digital Signal Processing. Some exposure to machine learning concepts is also desirable.
227-0224-00LStochastic Systems
Does not take place this semester.
W4 credits2V + 1Uto be announced
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0420-00LInformation Theory II Information W6 credits4GA. Lapidoth, S. M. Moser
AbstractThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
ObjectiveThe course's objective is to introduce the students to additional information measures and to equip them with the tools that are needed to conduct research in Information Theory as it relates to Communication Networks and to Statistics.
ContentSanov's Theorem, Rényi entropy and guessing, differential entropy, maximum entropy, the Gaussian channel, the entropy-power inequality, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, the Gelfand-Pinsker problem, and Fisher information.
Lecture notesn/a
LiteratureT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
Prerequisites / NoticeBasic introductory course on Information Theory.
227-0424-00LModel- and Learning-Based Inverse Problems in ImagingW4 credits2V + 1PV. Vishnevskiy
AbstractReconstruction is an inverse problem which estimates images from noisy measurements. Model-based reconstructions use analytical models of the imaging process and priors. Data-based methods directly approximate inversion using training data. Combining these two approaches yields physics-aware neural nets and state-of-the-art imaging accuracy (MRI, US, CT, microscopy, non-destructive imaging).
ObjectiveThe goal of this course is to introduce the mathematical models of imaging experiments and practice implementation of numerical methods to solve the corresponding inverse problem. Students will learn how to improve reconstruction accuracy by introducing prior knowledge in the form of regularization models and training data. Furthermore, students will practice incorporating imaging model knowledge into deep neural networks.
ContentThe course is based on following fundamental fields: (i) numerical linear algebra, (ii) mathematical statistics and learning theory, (iii) convex optimization and (iv) signal processing. The first part of the course introduces classical linear and nonlinear methods for image reconstruction. The second part considers data-based regularization and covers modern deep learning approaches to inverse problems in imaging. Finally, we introduce advances in the actively developing field of experimental design in biomedical imaging (i.e. how to conduct an experiment in a way to enable the most accurate reconstruction).

1. Introduction: Examples of inverse problems, general introduction. Refresh prerequisites.

2. Linear algebra in imaging: Refresh prerequisites. Demonstrate properties of operators employed in imaging.

3. Linear inverse problems and regularization: Classical theory of inverse problems. Introduce notion of ill-posedness and regularization.

3. Compressed sensing: Sparsity, basis-CS, TV-CS. Notion of analysis and synthesis forms of reconstruction problems. Application of PGD and ADMM to reconstruction.

4. Advanced priors and model selection: Total generalized variation, GMM priors, vectorial TV, low-rank, and tensor models. Stein's unbiased risk estimator.

5. Dictionary and prior learning: Classical dictionary learning. Gentle intro to machine learning. A lot of technical details about patch-models.

6. Deep learning in image reconstruction: Generic convolutional-NN models (automap, residual filtering, u-nets). Talk about the data generation process. Characterized difference between model- and data-based reconstruction methods. Mode averaging.

7. Loop unrolling and physics-aware networks for reconstruction: Autograd, Variational Networks, a lot of examples and intuition. Show how to use them efficiently, e.g. adding preconditioners, attention, etc.

8. Generative models and uncertainty quantification: Amortized posterior, variational autoencoders, adversarial learning. Estimation uncertainty quantification.

9. Inversible networks for estimation: Gradient flows in networks, inversible neural networks for estimation problems.

10. Experimental design in imaging: Acquisition optimization for continuous models. How far can we exploit autograd?

11. Signal sampling optimization in MRI. Reinforcement learning: Acquisition optimization for discrete models. Reinforce and policy gradients, variance minimization for discrete variables (RELAX, REBAR). Cartesian under-sampling pattern design

12. Summary and exam preparation.
Lecture notesLecture slides with references will be provided during the course.
Prerequisites / NoticeStudents are expected to know the basics of (i) numerical linear algebra, (ii) applied methods of convex optimization, (iii) computational statistics, (iv) Matlab and Python.
227-0427-10LAdvanced Signal Analysis, Modeling, and Machine Learning Information W6 credits4GH.‑A. Loeliger
AbstractThe course develops a selection of topics pivoting around graphical models (factor graphs), state space methods, sparsity, and pertinent algorithms.
ObjectiveThe course develops a selection of topics pivoting around factor graphs, state space methods, and pertinent algorithms:
- factor graphs and message passing algorithms
- hidden-​Markov models
- linear state space models, Kalman filtering, and recursive least squares
- Gaussian message passing
- Gibbs sampling, particle filter
- recursive local polynomial fitting & applications
- parameter learning by expectation maximization
- sparsity and spikes
- binary control and digital-​to-analog conversion
- duality and factor graph transforms
Lecture notesLecture notes
Prerequisites / NoticeSolid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning".
227-0432-00LLearning, Classification and Compression Information W4 credits2V + 1UE. Riegler
AbstractThe focus of the course is aligned to a theoretical approach of learning theory and classification and an introduction to lossy and lossless compression for general sets and measures. We will mainly focus on a probabilistic approach, where an underlying distribution must be learned/compressed. The concepts acquired in the course are of broad and general interest in data sciences.
ObjectiveAfter attending this lecture and participating in the exercise sessions, students will have acquired a working knowledge of learning theory, classification, and compression.
Content1. Learning Theory
(a) Framework of Learning
(b) Hypothesis Spaces and Target Functions
(c) Reproducing Kernel Hilbert Spaces
(d) Bias-Variance Tradeoff
(e) Estimation of Sample and Approximation Error

2. Classification
(a) Binary Classifier
(b) Support Vector Machines (separable case)
(c) Support Vector Machines (nonseparable case)
(d) Kernel Trick

3. Lossy and Lossless Compression
(a) Basics of Compression
(b) Compressed Sensing for General Sets and Measures
(c) Quantization and Rate Distortion Theory for General Sets and Measures
Lecture notesDetailed lecture notes will be provided.
Prerequisites / NoticeThis course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.
227-0558-00LPrinciples of Distributed Computing Information W7 credits2V + 2U + 2AR. Wattenhofer, M. Ghaffari
AbstractWe study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
ObjectiveDistributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week.
ContentDistributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems.

Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds
Lecture notesAvailable. Our course script is used at dozens of other universities around the world.
LiteratureLecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world.

Distributed Computing: Fundamentals, Simulations and Advanced Topics
Hagit Attiya, Jennifer Welch.
McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6

Introduction to Algorithms
Thomas Cormen, Charles Leiserson, Ronald Rivest.
The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8

Disseminatin of Information in Communication Networks
Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger.
Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes
Frank Thomson Leighton.
Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1

Distributed Computing: A Locality-Sensitive Approach
David Peleg.
Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8
Prerequisites / NoticeCourse pre-requisites: Interest in algorithmic problems. (No particular course needed.)
227-0560-00LDeep Learning for Autonomous Driving Information Restricted registration - show details
Registration in this class requires the permission of the instructors.
Class size will be limited to 80 students.
Please send an email to Dengxin Dai <> about your courses/projects that are related to machine learning, computer vision, and Robotics.
W6 credits3V + 2PD. Dai, A. Liniger
AbstractAutonomous driving has moved from the realm of science fiction to a very real possibility during the past twenty years, largely due to rapid developments of deep learning approaches, automotive sensors, and microprocessor capacity. This course covers the core techniques required for building a self-driving car, especially the practical use of deep learning through this theme.
ObjectiveStudents will learn about the fundamental aspects of a self-driving car. They will also learn to use modern automotive sensors and HD navigational maps, and to implement, train and debug their own deep neural networks in order to gain a deep understanding of cutting-edge research in autonomous driving tasks, including perception, localization and control.

After attending this course, students will:
1) understand the core technologies of building a self-driving car;
2) have a good overview over the current state of the art in self-driving cars;
3) be able to critically analyze and evaluate current research in this area;
4) be able to implement basic systems for multiple autonomous driving tasks.
ContentWe will focus on teaching the following topics centered on autonomous driving: deep learning, automotive sensors, multimodal driving datasets, road scene perception, ego-vehicle localization, path planning, and control.

The course covers the following main areas:

I) Foundation
a) Fundamentals of a self-driving car
b) Fundamentals of deep-learning

II) Perception
a) Semantic segmentation and lane detection
b) Depth estimation with images and sparse LiDAR data
c) 3D object detection with images and LiDAR data
d) Object tracking and Lane Detection

III) Localization
a) GPS-based and Vision-based Localization
b) Visual Odometry and Lidar Odometry

IV) Path Planning and Control
a) Path planning for autonomous driving
b) Motion planning and vehicle control
c) Imitation learning and reinforcement learning for self driving cars

The exercise projects will involve training complex neural networks and applying them on real-world, multimodal driving datasets. In particular, students should be able to develop systems that deal with the following problems:
- Sensor calibration and synchronization to obtain multimodal driving data;
- Semantic segmentation and depth estimation with deep neural networks ;
- 3D object detection and tracking in LiDAR point clouds
Lecture notesThe lecture slides will be provided as a PDF.
Prerequisites / NoticeThis is an advanced grad-level course. Students must have taken courses on machine learning and computer vision or have acquired equivalent knowledge. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. All practical exercises will require basic knowledge of Python and will use libraries such as PyTorch, scikit-learn and scikit-image.
252-0211-00LInformation Security Information W8 credits4V + 3UD. Basin, S. Capkun
AbstractThis course provides an introduction to Information Security. The focus
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
ObjectiveMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Content1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
252-0526-00LStatistical Learning Theory Information W8 credits3V + 2U + 2AJ. M. Buhmann, C. Cotrini Jimenez
AbstractThe course covers advanced methods of statistical learning:

- Variational methods and optimization.
- Deterministic annealing.
- Clustering for diverse types of data.
- Model validation by information theory.
ObjectiveThe course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning.
Content- Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing.

- Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures.

- Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation.

- Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models.
Lecture notesA draft of a script will be provided. Lecture slides will be made available.
LiteratureHastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.

L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Prerequisites / NoticeKnowledge of machine learning (introduction to machine learning and/or advanced machine learning)
Basic knowledge of statistics.
252-0538-00LShape Modeling and Geometry Processing Information W8 credits2V + 1U + 4AO. Sorkine Hornung
AbstractThis course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and polygonal meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing, topics in digital shape fabrication.
ObjectiveThe students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and geometry processing.
ContentRecent advances in 3D geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and triangle meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing and digital shape fabrication.
Lecture notesSlides and course notes
Prerequisites / NoticePrerequisites:
Visual Computing, Computer Graphics or an equivalent class. Experience with C++ programming. Solid background in linear algebra and analysis. Some knowledge of differential geometry, computational geometry and numerical methods is helpful but not a strict requirement.
252-0579-00L3D Vision Information W5 credits3G + 1AM. Pollefeys, V. Larsson
AbstractThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
ObjectiveAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
ContentThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
252-3005-00LNatural Language Processing Information Restricted registration - show details
Number of participants limited to 400.
W5 credits2V + 1U + 1AR. Cotterell
AbstractThis course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
ObjectiveThe objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
ContentThis course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LiteratureJacob Eisenstein: Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)
261-5130-00LResearch in Data Science Restricted registration - show details
Only for Data Science MSc.
W6 credits13AProfessors
AbstractIndependent work under the supervision of a core or adjunct faculty of data science.
ObjectiveIndependent work under the supervision of a core or adjunct faculty of data science.
An approval of the director of studies is required for a non DS professor.
ContentProject done under supervision of an approved professor.
Prerequisites / NoticeOnly students who have passed at least one core course in Data Management and Processing, and one core course in Data Analysis can start with a research project.

A project description must be submitted at the start of the project to the studies administration.
263-0007-00LAdvanced Systems Lab Information Restricted registration - show details
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
W8 credits3V + 2U + 2AM. Püschel, C. Zhang
AbstractThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.
ObjectiveSoftware performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.
ContentThe fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.

This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.

Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
Prerequisites / NoticeSolid knowledge of the C programming language and matrix algebra.
263-0008-00LComputational Intelligence Lab Information
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
W8 credits2V + 2U + 3AT. Hofmann
AbstractThis laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications.
ObjectiveStudents acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation.

This lab course has a strong focus on practical assignments. Students work in groups of three to four people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery.

For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper.

(Disclaimer: The offered projects may be subject to change from year to year.)
Contentsee course description
263-2925-00LProgram Analysis for System Security and Reliability Information W7 credits2V + 1U + 3AM. Vechev
AbstractSecurity issues in modern systems (blockchains, datacenters, deep learning, etc.) result in billions of losses due to hacks and system downtime. This course introduces fundamental techniques (ranging from automated analysis, machine learning, synthesis, zero-knowledge and their combinations) that can be applied in practice so to build more secure and reliable modern systems.
Objective* Understand the fundamental techniques used to create modern security and reliability analysis engines that are used worldwide.

* Understand how symbolic techniques are combined with machine learning (e.g., deep learning, reinforcement learning) so to create new kinds of learning-based analyzers.

* Understand how to quantify and fix security and reliability issues in modern deep learning models.

* Understand open research questions from both theoretical and practical perspectives.
ContentPlease see: for detailed course content.
263-3710-00LMachine Perception Information Restricted registration - show details
Number of participants limited to 200.
W8 credits3V + 2U + 2AO. Hilliges, S. Tang
AbstractRecent developments in neural networks (aka “deep learning”) have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and intelligent UIs. This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual tasks.
ObjectiveStudents will learn about fundamental aspects of modern deep learning approaches for perception. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision, robotics and HCI. The final project assignment will involve training a complex neural network architecture and applying it on a real-world dataset of human activity.

The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human input into computing systems. In particular, students should be able to develop systems that deal with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others.
ContentWe will focus on teaching: how to set up the problem of machine perception, the learning algorithms, network architectures and advanced deep learning concepts in particular probabilistic deep learning models

The course covers the following main areas:
I) Foundations of deep-learning.
II) Probabilistic deep-learning for generative modelling of data (latent variable models, generative adversarial networks and auto-regressive models).
III) Deep learning in computer vision, human-computer interaction and robotics.

Specific topics include: 
I) Deep learning basics:
a) Neural Networks and training (i.e., backpropagation)
b) Feedforward Networks
c) Timeseries modelling (RNN, GRU, LSTM)
d) Convolutional Neural Networks for classification
II) Probabilistic Deep Learning:
a) Latent variable models (VAEs)
b) Generative adversarial networks (GANs)
c) Autoregressive models (PixelCNN, PixelRNN, TCNs)
III) Deep Learning techniques for machine perception:
a) Fully Convolutional architectures for dense per-pixel tasks (i.e., instance segmentation)
b) Pose estimation and other tasks involving human activity
c) Deep reinforcement learning
IV) Case studies from research in computer vision, HCI, robotics and signal processing
LiteratureDeep Learning
Book by Ian Goodfellow and Yoshua Bengio
Prerequisites / Notice***
In accordance with the ETH Covid-19 master plan the lecture will be fully virtual. Details on the course website.

This is an advanced grad-level course that requires a background in machine learning. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. The course will focus on state-of-the-art research in deep-learning and will not repeat basics of machine learning

Please take note of the following conditions:
1) The number of participants is limited to 200 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge
3) All practical exercises will require basic knowledge of Python and will use libraries such as Pytorch, scikit-learn and scikit-image. We will provide introductions to Pytorch and other libraries that are needed but will not provide introductions to basic programming or Python.

The following courses are strongly recommended as prerequisite:
* "Visual Computing" or "Computer Vision"

The course will be assessed by a final written examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises.
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