Search result: Catalogue data in Spring Semester 2020

Number	Title	Type	ECTS	Hours	Lecturers
Robotics, Systems and Control Master
Core Courses
151-0116-10L	High Performance Computing for Science and Engineering (HPCSE) for Engineers II	W	4 credits	4G	P. Koumoutsakos, S. M. Martin
Abstract	This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.
Learning objective	The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences
Content	High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation
Lecture notes	https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs20/ Class notes, handouts
Literature	- Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
Prerequisites / Notice	Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)
151-0306-00L	Visualization, Simulation and Interaction - Virtual Reality I	W	4 credits	4G	A. Kunz
Abstract	Technology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).
Learning objective	The product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.
Content	Introduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.
Lecture notes	A complete version of the handout is also available in English.
Prerequisites / Notice	Voraussetzungen: keine Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF Testat/ Kredit-Bedingungen/ Prüfung: – Teilnahme an Vorlesung und Kolloquien – Erfolgreiche Durchführung von Übungen in Teams – Mündliche Einzelprüfung 30 Minuten
151-0534-00L	Advanced Dynamics	W	4 credits	3V + 1U	P. Tiso
Abstract	Lagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response
Learning objective	This course provides the students of mechanical engineering with fundamental analytical mechanics for the study of complex mechanical systems .We introduce the powerful techniques of principle of virtual work and virtual power to systematically write the equation of motion of arbitrary systems subjected to holonomic and non-holonomic constraints. The linearisation around equilibrium states is then presented, together with the concept of linearised stability. Linearized models allow the study of small amplitude vibrations for unforced and forced systems. For this, we introduce the concept of vibration modes and frequencies, modal superposition and modal truncation. The case of the vibration of light damped systems is discussed. The kinematics and dynamics of 3D rigid bodies is also extensively treated.
Lecture notes	Lecture notes are produced in class and are downloadable right after each lecture.
Literature	The students will prepare their own notes. A copy of the lecture notes will be available.
Prerequisites / Notice	Mechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent.
151-0566-00L	Recursive Estimation	W	4 credits	2V + 1U	R. D'Andrea
Abstract	Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Learning objective	Learn the basic recursive estimation methods and their underlying principles.
Content	Introduction 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 notes	Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html
Prerequisites / Notice	Requirements: Introductory probability theory and matrix-vector algebra.
151-0623-00L	ETH Zurich Distinguished Seminar in Robotics, Systems and Controls Does not take place this semester.	W	1 credit	1S	B. Nelson, M. Chli, R. Gassert, M. Hutter, W. Karlen, R. Riener, R. Siegwart
Abstract	This course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls.
Learning objective	Obtain an overview of various topics in Robotics, Systems, and Controls from leaders in the field. Please see Link for a list of upcoming lectures.
Content	This course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls. MSc students in Robotics, Systems, and Controls are required to attend every lecture. Attendance will be monitored. If for some reason a student cannot attend one of the lectures, the student must select another ETH or University of Zurich seminar related to the field and submit a one page description of the seminar topic. Please see Link for a suggestion of other lectures.
Prerequisites / Notice	Students are required to attend all seven lectures to obtain credit. If a student must miss a lecture then attendance at a related special lecture will be accepted that is reported in a one page summary of the attended lecture. No exceptions to this rule are allowed.
151-0630-00L	Nanorobotics	W	4 credits	2V + 1U	S. Pané Vidal
Abstract	Nanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field.
Learning objective	The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine.
151-0634-00L	Perception and Learning for Robotics Number of participants limited to: 30 To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to cesarc@ethz.ch for approval.	W	4 credits	9A	C. D. Cadena Lerma, J. J. Chung
Abstract	This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics.
Learning objective	Applying Machine Learning methods for solving real-world robotics problems.
Content	Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notes	Slides will be made available to the students.
Literature	Will be announced in the first lecture.
Prerequisites / Notice	The students are expected to be familiar with material of the "Recursive Estimation" and the "Introduction to Machine Learning" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required.
151-0641-00L	Introduction to Robotics and Mechatronics Number of participants limited to 60. Enrollment is only valid through registration on the MSRL website (www.msrl.ethz.ch). Registrations per e-mail is no longer accepted!	W	4 credits	2V + 2U	B. Nelson, N. Shamsudhin
Abstract	The aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use.
Learning objective	An ever-increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this course is to practically and theoretically expose students to the fundamentals of mechatronic and robotic systems. Over the course of the semester, the lecture topics will include an overview of robotics, an introduction to different types of sensors and their use, the programming of microcontrollers and interfacing these embedded computers with the real world, signal filtering and processing, an introduction to different types of actuators and their use, an overview of computer vision, and forward and inverse kinematics. Throughout the course, students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems. By the end of the course, you will be able to independently choose, design and integrate these different building blocks into a working mechatronic system.
Content	The course consists of weekly lectures and lab sessions. The weekly topics are the following: 0. Course Introduction 1. C Programming 2. Sensors 3. Data Acquisition 4. Signal Processing 5. Digital Filtering 6. Actuators 7. Computer Vision and Kinematics 8. Modeling and Control 9. Review and Outlook The lecture schedule can be found on our course page on the MSRL website (www.msrl.ethz.ch)
Prerequisites / Notice	The students are expected to be familiar with C programming.
151-0660-00L	Model Predictive Control	W	4 credits	2V + 1U	M. Zeilinger
Abstract	Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
Learning objective	Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Content	- Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Nonlinear MPC: Theory and computation - Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization - Simulation-based project providing practical experience with MPC
Lecture notes	Script / lecture notes will be provided.
Prerequisites / Notice	One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
151-0854-00L	Autonomous Mobile Robots	W	5 credits	4G	R. Siegwart, M. Chli, N. Lawrance
Abstract	The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
Learning objective	The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notes	This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
Literature	This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
151-1115-00L	Aircraft Aerodynamics and Flight Mechanics	W	4 credits	3G	J. Wildi
Abstract	Equations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability. Flight test. Wind tunnel test.
Learning objective	- Knowledge of methods to solve flight mechanic problems - To be able to apply basic methods for flight performence calculation and stability investigations - Basic knowledge of flight and wind tunnel tests and test evaluation methods
Content	Equations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability. Flight testing. Wind tunnel testing.
Lecture notes	Ausgewählte Kapitel der Flugtechnik (J. Wildi)
Literature	Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics (John Wiley and Sons), 1979 / 1995 Anderson, J: Fundamentals of Aerodynamics (McGraw-Hill Comp Inc), 2010
Prerequisites / Notice	Recommended: Lecture 'Basics of Aircraft und Vehicle Aerodynamics' (FS)
101-0521-10L	Machine Learning for Predictive Maintenance Applications The number of participants in the course is limited to 25 students. Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words): -How does this course fit to the other courses you have attended so far? -How does the course support you in achieving your goal? The following link can be used to upload the documents. https://polybox.ethz.ch/index.php/s/3S9ZlyxQTiOS3fM	W	8 credits	4G	O. Fink
Abstract	The course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime.
Learning objective	Students will - be able to understand the main challenges faced by predictive maintenance systems - learn to extract relevant features from condition monitoring data -learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics -learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples. - learn to design end-to-end machine learning algorithms for fault detection and diagnostics -be able to evaluate the performance of the applied algorithms. At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data.
Content	Early and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing. This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems. Specific topics include: -Introduction to condition monitoring and predictive maintenance systems -Feature extraction and selection methodology -Machine learning algorithms for fault detection and fault isolation -End-to-end learning architectures (including feature learning) for fault detection and fault isolation -Unsupervised and semi-supervised learning algorithms for predictive maintenance -Machine learning algorithms for prediction of the remaining useful life -Performance evaluation -Predictive maintenance systems at fleet level -Domain adaptation for fault diagnostics -Introduction to decision support systems for maintenance applications
Lecture notes	Slides and other materials will be available online.
Literature	Relevant scientific papers will be discussed in the course.
Prerequisites / Notice	Strong analytical skills. Programming skills in python are strongly recommended.
103-0848-00L	Industrial Metrology and Machine Vision Number of participants limited to 30.	W	4 credits	3G	K. Schindler, A. Wieser
Abstract	This course introduces contact and non-contact techniques for 3D coordinate, shape and motion determination as used for 3D inspection, dimensional control, reverse engineering, motion capture and similar industrial applications.
Learning objective	Understanding the physical basis of photographic sensors and imaging; familiarization with a broader view of image-based 3D geometry estimation beyond the classical photogrammetric approach; understanding the concepts of measurement traceability and uncertainty; acquiring an overview of general 3D image metrology including contact and non-contact techniques (coordinate measurement machines; optical tooling; laser-based high-precision instruments).
Content	CCD and CMOS technology; structured light and active stereo; shading models, shape from shading and photometric stereo; shape from focus; laser interferometry, laser tracker, laser radar; contact and non-contact coordinate measurement machines; optical tooling; measurement traceability, measurement uncertainty, calibration of measurement systems; 3d surface representations; case studies.
Lecture notes	Lecture slides and further literature will be made available on the course webpage.
227-0207-00L	Nonlinear Systems and Control Prerequisite: Control Systems (227-0103-00L)	W	6 credits	4G	E. Gallestey Alvarez, P. F. Al Hokayem
Abstract	Introduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems.
Learning objective	On completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics. Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods.
Content	Virtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives. During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course the students will be familiarized with the basic concepts of nonlinear control theory. This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control and mathematical analysis.
Lecture notes	An english manuscript will be made available on the course homepage during the course.
Literature	H.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Prerequisites / Notice	Prerequisites: Linear Control Systems, or equivalent.
227-0216-00L	Control Systems II	W	6 credits	4G	R. Smith
Abstract	Introduction to basic and advanced concepts of modern feedback control.
Learning objective	Introduction to basic and advanced concepts of modern feedback control.
Content	This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
Lecture notes	The slides of the lecture are available to download.
Literature	Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / Notice	Prerequisites: Control Systems or equivalent
227-0224-00L	Stochastic Systems	W	4 credits	2V + 1U	F. Herzog
Abstract	Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
Learning objective	Stochastic 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 notes	H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0248-00L	Power Electronic Systems II	W	6 credits	4G	J. W. Kolar
Abstract	This course details structures, operating ranges, and control concepts of modern power electronic systems to provide a deeper understanding of power electronic circuits and power components. Most recent concepts of high switching frequency AC/DC converters and AC/AC matrix inverters are presented. Simulation exercises, implemented in GeckoCIRCUITS, are used to consolidate the concepts discussed.
Learning objective	The objective of this course is to convey knowledge of structures, operating ranges, and control concepts of modern power electronic systems. Further objectives are: to know most recent concepts and operation modes of high switching frequency AC/DC converters and AC/AC matrix inverters; to develop a deeper understanding of multi-pulse power converter circuits, transformers, and electromechanical energy converters; and to understand in-depth details of power electronic systems. Simulation exercises, implemented in the electric circuit simulator GeckoCIRCUITS, are used to consolidate the presented theoretical concepts.
Content	Converter dynamics and control: State Space Averaging, transfer functions, controller design, impact of the input filter on the converter transfer functions. Performance data of single-phase and three-phase systems: effect of different loss components on the efficiency characteristics, linear and non-linear single phase loads, power flow of general three-phase systems, space vector calculus. Modeling and control of three-phase PWM rectifiers: system characterization using rotating coordinates, control structure, transfer functions, operation with symmetrical and unsymmetrical mains voltages. Scaling laws of transformers and electromechanical actuators. Drives with permanent magnet synchronous machines: basic function, modeling, field-oriented control. Unidirectional AC/DC converters and AC/AC converters: voltage and current DC link converters, indirect and direct matrix converters.
Lecture notes	Lecture notes and associated exercises including correct answers, simulation program for interactive self-learning including visualization/animation features.
Prerequisites / Notice	Prerequisites: Introductory course on power electronics.
227-0528-00L	Power System Dynamics, Control and Operation	W	6 credits	4G	G. Hug
Abstract	The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. This course is dedicated to the dynamical properties of the electric power grid including how the system state is estimated, generation/load balance is ensured by frequency control and how the system reacts in case of faults in the system. The course includes two excursions.
Learning objective	The learning objectives of the course are to understand and be able to apply the dynamic modeling of power systems, to compute and discuss the actions of generators based on frequency control, to describe the workings of a synchronous machine and the implications on the grid, to describe and apply state estimation procedures, to discuss the IT infrastructure and protection algorithms in power systems.
Content	The electric power system is a system that is never in steady state due to constant changes in load and generation inputs. Consequently, the monitoring and operation of the electric power grid is a challenging task. The course starts with the introduction of general operational procedures and the discussion of state estimation which is an important tool to observe the state of the grid. The course is then dedicated to the modeling and studying of the dynamical properties of the electric power grid. Frequency control which ensures the generation/load balance in real time is the basis for real-time control and is presented in depth. For the analysis of how the system detects and reacts dynamically in fault situations, protection and dynamic models for synchronous machines are introduced.
Lecture notes	Lecture notes. WWW pages.
227-0560-00L	Deep Learning for Autonomous Driving Registration in this class requires the permission of the instructors. Class size will be limited to 80 students. Preference is given to EEIT, INF and RSC students.	W	6 credits	3V + 2P	D. Dai, A. Liniger
Abstract	Autonomous 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.
Learning objective	Students 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.
Content	We 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 motion prediction 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 ; - Learning to drive with images and map data directly (a.k.a. end-to-end driving)
Lecture notes	The lecture slides will be provided as a PDF.
Prerequisites / Notice	This 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.
227-0690-11L	Advanced Topics in Control (Spring 2020) New topics are introduced every year.	W	4 credits	2V + 2U	G. Banjac
Abstract	Advanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor.
Learning objective	During Spring 2020 the course will cover a range of topics in large-scale convex optimization. The students should be able to apply various numerical methods to solve large-scale optimization problems arising in control, machine learning, signal processing, and finance.
Content	Convex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas.
Lecture notes	Copies of the projection slides will be made available on the course Moodle platform.
Literature	The course will be largely based on the Large-Scale Convex Optimization course taught at Lund University: https://archive.control.lth.se/ls-convex-2015/
Prerequisites / Notice	Sufficient mathematical maturity, in particular in linear algebra and analysis.

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