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Machine Learning (SS 2018)

News

  • 16.07.2018: Please check the guidelines for the upcoming final exam. For the final exam everything that has been discussed in the lecture or in the tutorials may be part of the exam! From the last chapter 'Bayes Nets' we just include bayesian networks and none of the following topics as they have not been discussed in more detail in the lecture (so up to the point of 'Markov chains'). Also there are no programming assignments in the final exam, so you do not have to learn the methods of keras/tensorflow by heart.
  • 15.06.2018: There will be no second exam for this lecture.
  • 14.06.2018: You can now register for the final exam via UniWorx.
  • 25.05.2018: The final exam will take place on July 23th from 11am to 1pm. Registration will be available via UniWorx.
  • 25.05.2018: The exercise sheet 4 has been updated
  • 18.05.2018: There will be no tutorials next week. The new exercise sheet handed out this week will be discussed the week after.
  • 02.05.2018: Please note that the lecture on May 17th will start an hour earlier. It will be from 8 am to 10 am
  • 25.04.2018: There will be no tutorials next week. The new exercise sheet handed out this week will be discussed the week after.
  • 13.04.2018: The lecture will be recorded. You can find the lecture videos at VideoOnline.
  • 16.02.2018: The registration for this course via UniWorx will be open from March 1st, 2018 onwards.

Organisation

  • Course: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Volker Tresp
  • Assistants: Christian Frey, Julian Busch
  • Required:  professional skill of at least one programming language
  • Audience: The course is directed towards master students in informatics, bioinformatics and media informatics

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Thu, 9,00 - 12,00 h Room S 002 (Schellingstr. 3) 12.04.2018
Tutorial 1 Tue, 14,00 - 16,00 h Room B 015 (Geschwister-Scholl-Platz 1) 17.04.2018
Tutorial 2 Tue, 16,00 - 18,00 h Room B 015 (Geschwister-Scholl-Platz 1) 17.04.2018
Tutorial 3 Wed, 14,00 - 16,00 h Room B 015 (Geschwister-Scholl-Platz 1) 18.04.2018
Tutorial 4 Wed, 16,00 - 18,00 h Room B 015 (Geschwister-Scholl-Platz 1) 18.04.2018

Content

Machine Learning is a data-driven approach for the development of technical solutions. Initially motivated by the adaptive capabilities of biological systems, machine learning has increasing impact in many fields, such as vision, speech recognition, machine translation, and bioinformatics, and is a technological basis for the emerging field of Big Data.

The lecture will cover:

  • Supervised learning: the goal here is to learn functional dependencies for classification and regression. We cover linear systems, basis function approaches, kernel approaches and neural networks. We will cover the recent developments in deep learning which lead to exciting applications in speech recognition and vision.
  • Unsupervised Learning: the goal here is to compactly describe important structures in the data. Typical representatives are clustering and principal component analysis
  • Graphical models (Bayesian networks, Markov networks), which permit a unified description of high-dimensional probabilistic dependencies
  • Reinforcement Learning as the basis for the learning-based optimization of autonomous agents
  • Some theoretical aspects: frequentist statistics, Bayesian statistics, statistical learning theory

The technical topics will be illustrated with a number of real-world applications.

 

Course Schedule

LectureTutorial
DateTopicDateTopic
12.04.18 Lecture 1: Introduction 17.04.18
18.04.18
Python Introduction (.ipynb)
Suggested Solutions (.ipynb)
19.04.18 Lecture 2: Linear Algebra (Review), Perceptron, Linear Regression 24.04.18
25.04.18
Exercise Sheet 1
Exercise 1-3 (.ipynb)
Suggested Solutions
Exercise 1-3 Solutions (.ipynb)
26.04.18 Lecture 3: Basis Functions, Neural Networks 01.05.18
02.05.18
no tutorials (May Day)
03.05.18 Lecture 4: Deep Learning, Manifolds 08.05.18
09.05.18
Exercise Sheet 2
Tensorflow Introduction (.ipynb)
Exercise 2-5 (.ipynb)
Suggested Solutions
Exercise 2-5 Solutions (.ipynb)
10.05.18 no lecture (Ascension Day) 15.05.18
16.05.18
Exercise Sheet 3
Exercise 3-3 (.ipynb)
Suggested Solutions
Exercise 3-3 Solutions (.ipynb)
17.05.18
8am-10am
Lecture 5: Kernels 22.05.18
23.05.18
no tutorials (Whit Tuesday)
24.05.18 Guest Lecture by Dr. Denis Krompaß: Deep Learning 29.05.18
30.05.18
Exercise Sheet 4
Exercise 4-1 (.ipynb)
Suggested Solutions
Exercise 4-1 Solutions (.ipynb)
31.05.18 no lecture (Corpus Christi) 05.06.18
06.06.18
Exercise Sheet 5
Suggested Solutions
07.06.18 Lecture 6: Probability (Review), Frequentists and Bayesians 12.06.18
13.06.18
Exercise Sheet 6
Suggested Solutions
14.06.18 Guest Lecture by Dr. Florian Buettner: PCA 19.06.18
20.06.18
Exercise Sheet 7
body_sizes.txt
Suggested Solutions
21.06.18 Lecture 7: Linear Classifiers, Support Vector Machine 26.06.18
27.06.18
Exercise Sheet 8
Suggested Solutions
28.06.18 Lecture 8: Model Comparison 03.07.18
04.07.18
Exercise Sheet 9
Suggested Solutions
05.07.18 Lecture 9: Bayes Nets 10.07.18
11.07.18
Exercise Sheet 10
Suggested Solutions

Final Examination

  • Date: July 23th, 2018, 11am - 1pm
  • Location: Geschwister-Scholl-Platz 1, main building, room B 101 and B 201
  • Registration via UniWorX
  • Guidelines for the exam

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