Advanced Machine Learning
|V3 + Ü1 (6 ECTS credits)|
Find a list of current courses on the Teaching page.
|Lecture / Exercise||Tuesday, 10:30 - 12:00||Zoom|
|Lecture / Exercise||Thursday, 14:30 - 16:00||Zoom|
This lecture will extend the scope of the "Machine Learning" lecture with additional and, in parts, more advanced concepts. In particular, the lecture will cover the following areas:
- Regression techniques (linear regression, ridge regression, lasso, support vector regression)
- Probabilistic Graphical Models
- Exact inference
- Approximative Inference
- Learning with Latent Variables
- Deep Generative Models
- Deep Reinforcement Learning
We will mainly make use of the following books:
- C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
- I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, 2016
- R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018
However, a good part of the material presented in this class is the result of very recent research, so it hasn't found its way into textbooks yet. Wherever research papers are necessary for a deeper understanding, we will make them available on this web page.
Successful completion of the class "Machine Learning" is recommended, but not a hard prerequisite.
The class is accompanied by exercises that will allow you to collect hands-on experience with the algorithms introduced in the lecture. There will be both pen&paper exercises and practical programming exercises based on Matlab/numpy/TensorFlow (roughly 1 exercise sheet every 2 weeks). Please turn in your solutions to the exercises by e-mail to the appropriate TA the night before the exercise class.
We ask you to work in teams of 2-3 students.
|Introduction||First introductory lecture|
|Linear Regression I||Probabilistic View of Regression, Maximum Likelihood, MAP, Bayesian Curve Fitting|
|Linear Regression II||Basis Functions, Sequential Learning, Multiple Outputs, Regularization, Lasso, Bias-Variance Decomposition|