| NUMMER: | 141213 |
| KÜRZEL: | FDS |
| MODULBEAUFTRAGTE:R: | M. SC. Jan Richter-Brockmann |
| DOZENT:IN: | Prof. Dr.-Ing. Aydin Sezgin Chu Li |
| FAKULTÄT: | Fakultät für Informatik |
| SPRACHE: | Englisch |
| SWS: | 4 |
| CREDITS: | 5 |
| ANGEBOTEN IM: | jedes Wintersemester |
VERANSTALTUNGSART
rechnerbasierte Präsentation
PRÜFUNGEN
| FORM: | mundlich |
| TERMIN: | Siehe Prüfungsamt. |
LERNFORM
Vorlesungen und Ubungen
LERNZIELE
The students understand the concepts of pattern recognition, machine learning, andinformation theory and are able to apply it to data analysis. Equipped with tools and methods acquired during the lectures, problems arising regularly in engineering disciples can be
investigated.
INHALT
The view taken in the course is based on the ideas that data science is fundamentally rooted in information theory, as information theory is the pillar of most machine learningalgorithms. Naturally, stochastic processes will also play a role, as sequences of events can be
modeled nicely. The course has also a focus on Bayesian statistics and includes new developments in neural networks and deep learning.
The table of contents is as follows:
• Introduction
• Review: Linear Algebra
• Review: Probability Theory, Random variables and, Markov Chains, processes (Gaussian,
Markov Decision)
• Least Mean Square Estimation
• Classification
• Bayesian Learning
• Information theoretic learning
– Kullback-Leibler Divergence
– ICA, Dictionary Learning,
– k-SVD, Rate distortion theory,
– entropy maximization, information bottleneck
• Neural networks and deep learning
As part of the exercise sessions, the students will implement various algorithms in Matlab:
• LMS, Kalman, Stochastic Gradient Descent,
• k-Means, KNN,
• Expectation Maximization, Backpropagation etc.
The focus of the course is on
• Discovery of regularities in data via Pattern recognition
• Development of algorithms via Machine learning (Classification, Clustering, Reinforcement Learning)
• Performance criteria via Information theory
• Hands-on experience
The main references for the course are:
• Sergios Theodoridis, Machine Learning- A Bayesian and optimization perspective.
• Simon Haykin, Neural Networks and Learning Machines
• Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning
VORAUSSETZUNGEN CREDITS
Bestandene mündliche Prüfung
EMPFOHLENE VORKENNTNISSE
-Math I-IV -System theory I-III -Optimization