Fundamentals of Data Science

NUMMER: 141213
KÜRZEL: FDS
MODULBEAUFTRAGTE:R: Prof. Dr.-Ing. Aydin Sezgin
Chu Li
DOZENT:IN: M. SC. Jan Richter-Brockmann
FAKULTÄT: Fakultät für Informatik
SPRACHE: Englisch
SWS: 4
CREDITS: 5
WORKLOAD: 150 Stunden
ANGEBOTEN IM: jedes Wintersemester

PRÜFUNGEN

FORM: mundlich
ANMELDUNG:
DATUM: 0000-00-00
BEGINN: 00:00:00
DAUER: 30 Min
RAUM:

LERNFORM

Vorlesungen und Ubungen

LERNZIELE

The students understand the concepts of pattern recognition, machine learning, and
information 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 learning
algorithms. 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

LITERATUR

[1] C. M., Bishop ”Pattern Recognition and Machine Learning”, Springer Verlag, 2006