Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Maschineningenieurwissenschaften Master Information
Kernfächer
Robotics, Systems and Control
Die unter der Kategorie “Kernfächer” gelisteten Fächer sind empfohlen. Andere Kurse sind nicht ausgeschlossen, benötigen jedoch die Zustimmung des Tutors/der Tutorin.
NummerTitelTypECTSUmfangDozierende
151-0310-00LModel Predictive Engine Control Belegung eingeschränkt - Details anzeigen
Number of participants limited to 55.
W4 KP2V + 1UT. Albin Rajasingham
KurzbeschreibungFor efficient and stable operation of an internal combustion engine a multitude of complex control tasks have to be handled. In this lecture the application of model predictive control for these control challenges is introduced.
Lernziel- Learn how to design and implement model predictive control algorithms for the example system “combustion engine”. Get to know the entire process from simulation-based control development to the application at a real-world combustion engine.
- Deepen the knowledge concerning the necessary control algorithms for a combustion engine.
Inhalt- Physical phenomena and models for processes of the combustion engine such as air path and fuel path
- Analysis of the control tasks arising in engine systems
- Case studies for the application of model predictive control for combustion engines with the goal to handle the complex, multivariable system dynamics
- Fundamentals of the implementation of model predictive control
SkriptLecture slides will be provided after each lecture.
LiteraturL. Guzzella / C. Onder: "Introduction to Modeling and Control of Internal Combustion Engine Systems", J. Maciejowski: "Predictive Control with Constraints"
Voraussetzungen / BesonderesEngine Systems (recommended).
151-0314-00LInformationstechnologien im digitalen ProduktW4 KP3GE. Zwicker, R. Montau
KurzbeschreibungZielsetzung, Konzepte und Methoden der Digitalisierung, Digitales Produkt und Product Lifecycle Management (PLM), Industrie 4.0
Digitalisierungskonzepte: Produktstrukturen, Prozessoptimierung mit digitalen Modellen in Verkauf, Produktion, Service, Digital Twin versus Digital Thread
PLM-Grundlagen: Objekte, Strukturen, Prozesse, Integrationen, Visualisierung
Praktische Anwendungen
LernzielStudierenden lernen die Grundlagen und Konzepte der Digitalisierung im Produktlebenszylus auf Basis von Produkt Lifecycle Management-Technologien (PLM), den Einsatz von Datenbanken, die Integration von CAx-Systemen und Visualisierung/AR, den Aufbau computergestützter Kollaboration auf Basis von Standards und Protokollen sowie das Varianten- und Konfigurationsmanagement zur effizienten Nutzung des Digitalen Produkt-Ansatzes für Industrie 4.0.
InhaltMöglichkeiten und Potenziale moderner IT-Applikationen mit Fokus auf PLM- und CAx--Technologien für den zielgerichteten Einsatz im Zusammenhang Produktplattform - Unternehmensprozesse - IT-Tools. Einführung in die Konzepte des Product Lifecycle Managements (PLM): Informationsmodellierung, Datenmanagement, Revisionierung, Nutzung und Verteilung von Produktdaten. Aufbau und Funktionsweise von PLM-Systemen. Integration neuer IT-Technologien in Unternehmensprozesse. Möglichkeiten der Publikation und automatischen Konfiguration von Produktvarianten im Internet. Einsatz modernster Informations- und Kommunikationstechnologien beim Entwickeln von Produkten an global verteilten Standorten. Schnittstellen der rechnerintegrierten Produktentwicklung. Auswahl, Projektierung, Anpassung und Einführung von PLM-Systemen. Beispiele und Fallstudien für den industriellen Einsatz moderner Informationstechnologien.

Lehrmodule:
- Einführung in die Digitalisierung (Digitales Produkt, PLM)
- Datenbanktechnologie (Basis der Digitalisierung)
- Objektmanagement
- Objektklassifikation
- Objektidentifikation mit Sachnummernsystem
- CAx/PLM-Integration mit Visualisierung/AR
- Workflow & Change Management
- Schnittstellen im Digitalen Produkt
- Enterprise Application Integration (EAI)
SkriptDidaktisches Konzept/Lehrmaterialien:
Die Durchführung der Lehrveranstaltung erfolgt gemischt mit Vorlesungs- und Übungsanteilen anhand von Praxisbeispielen.
Bereitstellung von Vorlesungs-Handouts und Skriptum digital in Moodle.
Voraussetzungen / BesonderesVoraussetzungen: Keine
Empfohlen: Fokus-Projekt, Interesse an Digitalisierung
Vorlesung geeignet für D-MAVT, D-MTEC, D-ITET und D-INFK

Testat/Kredit-Bedingungen / Prüfung:
- Durchführung von Übungen in Teams (empfohlen)
- Mündliche Einzelprüfung 30 Minuten, anhand konkreter Problemstellungen
151-0318-00LEcodesign - Umweltgerechte ProduktgestaltungW4 KP3GR. Züst
KurzbeschreibungEcodesign hat zum Ziel, die Umweltleistung von Produkten insgesamt zu verbessern. Zugleich soll die ökonomische und marktseitige Situation verbessert werden.
Die Vorlesung gliedert sich in drei Teile: Motivation und Einstieg ins Thema, methodische Grundlagen, sowie Anwendung in einem eigenen Kleinprojekt.
LernzielEs setzt sich die Erkenntnis durch, dass ein bedeutender Teil der Umweltbelastungen eines Unternehmens durch die eigenen Produkte in vor- und nachgelagerten Bereichen verursacht werden. Das Ziel von Ecodesign besteht darin, die Umweltauswirkungen eines Produktes über alle Produktlebensphasen insgesamt zu reduzieren. Die systematische Herleitung erfolgversprechender Verbesserungsmaßnahmen zu Beginn des Produktentwicklungsprozesses ist eine Schlüsselfähigkeit, die in der vorliegenden Vorlesung vermittelt werden soll.
Die Teilnehmerinnen und Teilnehmer sollen die ökonomischen und ökologischen Potentiale von ECODESIGN erkennen, Fähigkeiten erlernen, zielgerichtet erfolgversprechende Verbesserungsmaßnahmen zu ermitteln und die erworbenen Fähigkeiten an konkreten Beispielen anwenden können.
InhaltDie Vorlesung ist in drei Blöcke unterteilt. Hier sollen die jeweiligen Fragen beantwortet werden:
A) Motivation und Einstieg ins Thema: Welche Material- und Energieflüsse werden durch Produkte über alle Lebensphasen, d.h. von der Rohstoffgewinnung, Herstellung, Distribution, Nutzung und Entsorgungen verursacht? Welchen Einfluss hat die Produktentwicklung auf diese Auswirkungen?
B) Grundlagen zum ECODESIGN PILOT: Wie können systematisch – über alle Produktlebensphasen hinweg betrachtet – bereits zu Beginn der Produktentwicklung bedeutende Umweltauswirkungen erkannt werden? Wie können zielgerichtet diejenigen Ecodesign-Maßnahmen ermittelt werden, die das größte ökonomische und ökologische Verbesserungspotential beinhalten?
C) Anwendung des ECODESIGN PILOT: Welche Produktlebensphasen bewirken den größten Ressourcenverbrauch? Welche Verbesserungsmöglichkeiten bewirken einen möglichst großen ökonomischen und ökologischen Nutzen?
Im Rahmen der Vorlesung werden verschiedene Praktische Beispiel bearbeitet.
SkriptFür den Einstieg ins Thema ECODESIGN wurde verschiedene Lehrunterlagen entwickelt, die im Kurs zur Verfügung stehen und teilwesie auch ein "distance learning" ermöglichen:

Lehrbuch: Wimmer W., Züst R.: ECODESIGN PILOT, Produkt-Innovations-, Lern- und Optimierungs-Tool für umweltgerechte Produktgestaltung mit deutsch/englischer CD-ROM; Zürich, Verlag Industrielle Organisation, 2001. ISBN 3-85743-707-3

CD: im Lehrbuch inbegriffen (oder Teil "Anwenden" on-line via: Link)
Internet: Link vermittelt verschiedene weitere Zugänge zum Thema. Zudem werden CD's abgegeben, auf denen weitere Lehrmodule vorhanden sind.
LiteraturHinweise auf Literaturen werden on-line zur Verfügung gestellt.
Voraussetzungen / BesonderesTestatbedingungen: Abgabe von zwei Übungen
151-0530-00LNonlinear Dynamics and Chaos IIW4 KP4GG. Haller
KurzbeschreibungThe internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems
LernzielThe course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis.
InhaltI. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations.

II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory.

III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications.
IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows
SkriptStudents have to prepare their own lecture notes
LiteraturBooks will be recommended in class
Voraussetzungen / BesonderesNonlinear Dynamics I (151-0532-00) or equivalent
151-0534-00LAdvanced DynamicsW4 KP3V + 1UP. Tiso
KurzbeschreibungLagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response
LernzielThis 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.
SkriptLecture notes are produced in class and are downloadable right after each lecture.
LiteraturThe students will prepare their own notes. A copy of the lecture notes will be available.
Voraussetzungen / BesonderesMechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent.
151-0566-00LRecursive Estimation Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
LernzielLearn the basic recursive estimation methods and their underlying principles.
InhaltIntroduction 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.
SkriptLecture notes available on course website: Link
Voraussetzungen / BesonderesRequirements: Introductory probability theory and matrix-vector algebra.
151-0630-00LNanorobotics Information W4 KP2V + 1US. Pané Vidal
KurzbeschreibungNanorobotics 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.
LernzielThe 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.
151-0634-00LPerception and Learning for Robotics Belegung eingeschränkt - Details anzeigen
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 Link for approval.
W4 KP9AC. D. Cadena Lerma, J. J. Chung
KurzbeschreibungThis 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.
LernzielApplying Machine Learning methods for solving real-world robotics problems.
InhaltDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
SkriptSlides will be made available to the students.
LiteraturWill be announced in the first lecture.
Voraussetzungen / BesonderesThe 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.
151-0641-00LIntroduction to Robotics and Mechatronics Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 60.

Enrollment is only valid through registration on the MSRL website (Link). Registrations per e-mail is no longer accepted!
W4 KP2V + 2UB. Nelson, N. Shamsudhin
KurzbeschreibungThe 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.
LernzielAn 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.
InhaltThe 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 (Link)
Voraussetzungen / BesonderesThe students are expected to be familiar with C programming.
151-0660-00LModel Predictive Control Information W4 KP2V + 1UM. Zeilinger
KurzbeschreibungModel 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.
LernzielDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Inhalt- 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
SkriptScript / lecture notes will be provided.
Voraussetzungen / BesonderesOne 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).
151-0854-00LAutonomous Mobile Robots Information W5 KP4GR. Siegwart, M. Chli, N. Lawrance
KurzbeschreibungThe 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.
LernzielThe 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.
SkriptThis 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.
LiteraturThis 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
151-1115-00LAusgewählte Kapitel der FlugtechnikW4 KP3GJ. Wildi
KurzbeschreibungBewegungsgleichungen. Flugleistungen und Flugbereiche. Statische Stabilität und Steuerbarkeit (Längs-, Lateral, Geschwindigkeits-, Windfahnenstabilität). Dynamische Längs- und Querstabilität.
Einführung in die Flug- und Windkanalmesstechnik.
Lernziel- Grundlagen vermitteln zur Lösung flugmechanischer Aufgabenstellungen
- Überblick geben über Methoden zur Behandlung von flugdynamischen Stabilitätsproblemen
- Durchführen von Flugleistungsberechnungen
- Einführen von Verfahren der Flugmesstechnik und Auswertung von Versuchen.
InhaltBewegungsgleichungen. Flugleistungen und Flugbereiche. Statische Stabilität und Steuerbarkeit (Längs-, Lateral, Geschwindigkeits-, Windfahnenstabilität). Dynamische Längs- und Querstabilität.
Einführung in die Flug- und Windkanalmesstechnik.
SkriptAusgewählte Kapitel der Flugtechnik (J. Wildi)
Voraussetzungen / BesonderesEmpfohlen: Vorlesung 'Grundlagen der Flugzeug- und Fahrzeugaerodynamik' (FS)
151-0116-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 KP4GP. Koumoutsakos, S. M. Martin
KurzbeschreibungThis 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.
LernzielThe 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
InhaltHigh 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
SkriptLink
Class notes, handouts
Literatur- 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
Voraussetzungen / BesonderesStudents must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)
101-0521-10LMachine Learning for Predictive Maintenance Applications Belegung eingeschränkt - Details anzeigen
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.
Link
W8 KP4GO. Fink
KurzbeschreibungThe 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.
LernzielStudents 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.
InhaltEarly 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
SkriptSlides and other materials will be available online.
LiteraturRelevant scientific papers will be discussed in the course.
Voraussetzungen / BesonderesStrong analytical skills.
Programming skills in python are strongly recommended.
103-0848-00LIndustrial Metrology and Machine Vision Belegung eingeschränkt - Details anzeigen
Number of participants limited to 30.
W4 KP3GK. Schindler, A. Wieser
KurzbeschreibungThis 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.
LernzielUnderstanding 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).
InhaltCCD 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.
SkriptLecture slides and further literature will be made available on the course webpage.
227-0216-00LControl Systems II Information W6 KP4GR. Smith
KurzbeschreibungIntroduction to basic and advanced concepts of modern feedback control.
LernzielIntroduction to basic and advanced concepts of modern feedback control.
InhaltThis 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.
SkriptThe slides of the lecture are available to download.
LiteraturSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Voraussetzungen / BesonderesPrerequisites:
Control Systems or equivalent
227-0224-00LStochastic SystemsW4 KP2V + 1UF. Herzog
KurzbeschreibungProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
LernzielStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Inhalt- 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
SkriptH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0690-11LAdvanced Topics in Control (Spring 2020)
New topics are introduced every year.
W4 KP2V + 2UG. Banjac
KurzbeschreibungAdvanced 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.
LernzielDuring 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.
InhaltConvex 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.
SkriptCopies of the projection slides will be made available on the course Moodle platform.
LiteraturThe course will be largely based on the Large-Scale Convex Optimization course taught at Lund University: Link
Voraussetzungen / BesonderesSufficient mathematical maturity, in particular in linear algebra and analysis.
252-0220-00LIntroduction to Machine Learning Information Belegung eingeschränkt - Details anzeigen
Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact Link
W8 KP4V + 2U + 1AA. Krause
KurzbeschreibungThe course introduces the foundations of learning and making predictions based on data.
LernzielThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Inhalt- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor
- Neural networks (backpropagation, regularization, convolutional neural networks)
- Unsupervised learning (k-means, PCA, neural network autoencoders)
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
LiteraturTextbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Voraussetzungen / BesonderesDesigned to provide a basis for following courses:
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
252-0526-00LStatistical Learning Theory Information W7 KP3V + 2U + 1AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungThe course covers advanced methods of statistical learning:

- Variational methods and optimization.
- Deterministic annealing.
- Clustering for diverse types of data.
- Model validation by information theory.
LernzielThe 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.
Inhalt- 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.
SkriptA draft of a script will be provided. Lecture slides will be made available.
LiteraturHastie, 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
Voraussetzungen / BesonderesKnowledge of machine learning (introduction to machine learning and/or advanced machine learning)
Basic knowledge of statistics.
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