Vorlesung: Statistical Machine Learning

Quick Facts

Lecturer:Jan Peters
Teaching Assistants:Daniel Tanneberg, Boris Belousov
Lectures:Wednesday, 13:30-17:00 in Room S103/226
Language:English
Office Hours:Daniel Tanneberg, Request by Email
 Boris Belousov, Request by Email
TU-CAN:20-00-0358-iv Machine Learning: Statistical Approaches
Credits:6,0 ECTS (4 SWS)

Exam:Fri, 20. Jul. 2018, 13:00-15:00 in Rooms:
Last NameRoom
A - KrS105/122
L - ZS206/030

Description

As the World Wide Web keeps growing, computer science keeps evolving from is traditional form, slowly slowly becoming the art to create intelligent software and hardware systems that draw relevant information from the enormous amount of available data.

Why? Let's look at the facts: billions of web pages are at our disposal, videos with an accumulated time of 20 hours are uploaded every minute on Youtube and the supermarket chain Walmart alone performed more than one million transactions per hour, creating a database of more than 2.5 petabytes of information. John Naisbitt has stated the problem very clearly:

"We are drowning in information and starving for knowledge."

In the future of computer science, machine learning will therefore be an important core technology. Not only that, machine learning already is the technology which promises the best computer science jobs. Hal Varian, the Chief Engineer of Google in 2009 depicted it like this:

"I keep saying the sexy job in the next ten years will be statisticians and machine learners. People think I am joking, but who would have guessed that computer engineers would have been the sexy job of the 1990s? The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it, that is going to be a hugely important skill in the next decades. "

Accordingly, this lecture serves as an introduction to machine learning. Special emphasis is placed on a clear presentation of the lectures contents supplemented by small sample problems regarding each of the topics. The teacher pays particular attention to his interactions with the participants of the lecture, asking multiple question and appreciating enthusiastic students.

Contents

The course gives an introduction to statistical machine learning methods. The following topics are expected to be covered throughout the semester:

  • Probability Distributions
  • Linear Models for Regression and Classification
  • Kernel Methods, Graphical Models
  • Mixture Models and EM
  • Approximate Inference
  • Continuous Latent Variables
  • Hidden Markov Models

Requirements

Math classes from the bachelor's degree, basic programming abilities, introductory classes to computer science.

To see information on the teaching material, homeworks and final exam info, please login with your TU-ID!

Literature

The most important books for this class are:

  1. C.M. Bishop. Pattern Recognition and Machine Learning, Springer
  2. K.P. Murphy. Machine Learning: a Probabilistic Perspective, MIT Press

Additionally, the following books might be useful for specific topics:

  1. D. Barber. Bayesian Reasoning and Machine Learning, Cambridge University Press Free online copy
  2. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Springer Verlag Free online copy
  3. D. MacKay. Information Theory, Inference, and Learning Algorithms, Cambridge University Press Free online copy
  4. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification, Willey-Interscience
  5. T. M. Mitchell. Machine Learning, McGraw-Hill
  6. R. Sutton, A. Barto. Reinforcement Learning - An Introduction, MIT Press Free online copy
  7. M. Jordan. An Introduction to Probabilistic Graphical Models Free online copy


Teaching Staff

Lectures will be held by Jan Peters and additionally supervised by Daniel Tanneberg and Boris Belousov.

Jan Peters heads the Intelligent Autonomous Systems Lab at the Department of Computer Science at the TU Darmstadt. Jan has studied computer science, electrical, control, mechanical and aerospace engineering. You can find Jan Peters in the Robert-Piloty building S2 | 02 room E314. You can also contact him through mail@jan-peters.net.

Daniel Tanneberg is a Ph.D. student at the Intelligent Autonomous Systems (IAS) Group at the Technical University of Darmstadt since October 2015. He is investigating the applicability and properties of (spiking/stochastic) deep neural networks for open-ended robot learning. He is working on the GOAL-Robots project, that aims at developing goal-based open-ended autonomous learning robots; building lifelong learning robots. You can contact him by email at daniel@robot-learning.de .


Boris Belousov is interested in the theory and applications of motor skill learning in robotics. Deep insights from classical control, information theory, and statistics are bringing us to the next level of autonomy, enabling robots to perform increasingly delicate, skilled tasks. However, our understanding of generalization and compositionality in movement generation and execution is lagging behind. If you are interested in bridging the gap, get in touch with boris@robot-learning.de.


For further inquiries do not hesitate to contact us immediately!

  

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