Machine Learning (SS 2019)
- 17.06.2019: This week's tutorials will regularly take place. We are highly sorry for the delay in uploading exercise sheet 07
- 07.06.2019: A proof of the invertibility of the matrix occuring in the analytical solution of the linear regression problem with regularization can be found under tutorial 3. Please note that a modified version of the identity matrix has to be used.
- 17.05.2019: The final exam will be on August 6th, 2019 from 10am - 12pm. Further details will be announced on time.
- 25.04.2019: Important - the tutorials will start on May 7th, resp., May 8th, due to the Labor Day on May 1st
- 25.04.2019: The lecture will be recorded. You can find the videos here.
- 28.02.2019: The registration for this course via UniWorx will be open from April 1st, 2019 onwards.
- Course: 3+2 hours weekly (equals 6 ECTS)
- Lecture: Prof. Dr. Volker Tresp
- Assistants: Christian Frey, Sabrina Friedl
- Required: Professional skill of at least one programming language
- Audience: The course is directed towards master students in informatics, bioinformatics and media informatics
- Course Language: English
Time and Locations
All times are c.t. (cum tempore)
|Lecture||Thu, 9:00 - 12:00 h||Room M 218 (Geschwister-Scholl-Platz 1)||25.04.2019|
|Tutorial 1||Tue, 14:00 - 16:00 h||Room B 015 (Geschwister-Scholl-Platz 1)||07.05.2019|
|Tutorial 2||Tue, 16:00 - 18:00 h||Room B 015 (Geschwister-Scholl-Platz 1)||07.05.2019|
|Tutorial 3||Wed, 14:00 - 16:00 h||Room B 015 (Geschwister-Scholl-Platz 1)||08.05.2019|
|Tutorial 4||Wed, 16:00 - 18:00 h||Room B 015 (Geschwister-Scholl-Platz 1)||08.05.2019|
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.
---Labor Day May 1st---
|02.05.2019||Perceptron (update!), Linear Algebra, Linear Regression (update!)||rec02||07.05.2019
|Exercise Sheet 01,
|09.05.2019||Linear Regression (cont.), Basis Functions, Neural Networks||rec03||14.05.2019
|Exercise Sheet 02,
|16.05.2019||Neural Networks (cont.), Deep Learning||rec04||21.05.2019
|Exercise Sheet 03,
|23.05.2019||Deep Learning (cont.)||rec05||28.05.2019
|Exercise Sheet 04,
ex04_pytorch.ipynb (updated!) ex04_pytorch_solution.ipynb
|30.05.2019||No lecture: Ascension Day||---||04.06.2019
|Exercise Sheet 05,
|Exercise Sheet 06|
|13.06.2019||Kernels (cont.), Manifolds||rec07||18.06.2019
|Exercise Sheet 07|
|20.06.2019||No lecture: Corpus Christi||---||25.06.2019
The lecture will be recorded. The videos will be uploaded after each lecture and can be found here
- Date: August 6th, 2019
- Time: 10am - 12pm
- Location: Audimax (A030), B 201
- Registration will be via UniWorx (For students with no UniWorX account it is mandatory to write an email to the assistants such that we can register you manually)
- Guidelines: tba
Please note: There will be no second exam!
- Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville
- The Elements of Statistical Learning: Data mining, Inference and Prediction. Hastie, Tibshirani, Friedman
- Machine Learning: a Probabilistic Perspective. Kevin Murphy
- Bayesian Reasoning and Machine Learning. David Barber
- Pattern Recognition and Machine Learning. Bishop
- Artificial Intelligence: A Modern Approach. Russel and Norvig