Machine Learning Course SS 19 U Stuttgart



See my general teaching page for previous versions of this lecture. Esp see the compiled slides for an overview.


The tutorials on Monday, Apr 8 are cancelled. The first lecture is on Thu, Apr 11, 14:00, hall V38.04.

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Lecture
Weekly, Thursdays, 14:00, hall V38.04
Tutorials
Starting from the 2nd week, weekly. Please join the first lecture to learn about how to choose the session, how to get credits, etc. These are the sessions:

Ü1 -- Mo 09:45 -- room V7.03 -- Janik
Ü2 -- Mo 11:30 -- room V7.03 -- Janik
Ü3 -- Mo 14:00 -- room 0.124 -- Marc -- special session for data science students
Ü4 -- Mo 17:30 -- room V38.04 -- Philipp

Schedule, slides & exercises
date topics slides exercises
(due on following Monday)
11.4. Introduction 01-introduction e01-intro
18.4. Regression 02-regression holiday
25.4. Classification 03-classification e02-linearRegression
../data/dataLinReg2D.txt
../data/dataQuadReg2D.txt
../data/dataQuadReg2D_noisy.txt
2.5. Classification e03-classification
9.5. Neural Networks 04-neuralNetworks e04-classification
../data/data2Class.txt
16.5. Neural Networks e05-NN
23.5. Kernelization 05-kernelization e06-tensorFlow
30.5. (holiday) e07-kernels
6.6. Unsupervised Learning 06-unsupervised e08-PCA
20.6. (holiday) e09-clustering
../data/mixture.txt
27.6. Lazy Learning & Boosting 07-localLearning-ensembles e10-knn-boosting
../data/data2ClassHastie.txt
4.7. Probabilistic ML 08-probabilisticML
10-probabilities
e11-gaussianProcesses
11.7. Probabilistic ML e12-bonus
18.7. Summary & Q&A with tutors 11-recap
Script & Exam Preparation
Here is the complete script for this year: paper .

Use the table of contents as an overview. Topics we skipped or discussed only vert briefly are marked **, and are not relevant for the exam.

See page 137!

Here is an example exam from before 2016: ../16-MachineLearning/beispielKlausur