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Department of Information Technology

Statistical Machine Learning - Lectures

The course comprises 10 lectures, plus 1 additional lecture for introducing the programming language python. See the literature page for a list of recommended reading for each lecture.

For each lecture, we will in advance post one (or a few) short warm-up videos from the internet, which you may have a look at if you wish to come better prepared to the lecture. The videos are meant to be inspiring and to give an initial idea of the topic, but they are not a replacement of the lecture.

# Lecture Lecturer Warm-up video Slides
1. Introduction JW 5 min, 2 min Le1
2. Linear regression, regularization TS 4 min, 1 min Le2
- Introduction to python JW, NW A full python course Le-Python ../exercises/notebook_badge_24.png ../exercises/colab_badge_24.png
3. Classification, logistic regression TS 3 min, 15 min Le3
4. Classification, LDA, QDA, k-NN JW 15 min* 5 min Le4
5. Bias-variance trade-off, cross validation JW 6 min, 3 min Le5
6. Tree-based methods, bagging NW 10 min 3 min Le6
7. Boosting TS 2 min, 5 min Le7
8. Deep learning I NW 19 min Le8
9. Deep learning II NW 21 min Le9
10. Summary and guest lecture (SEB) JW Le10

JW = Johan Wågberg
TS = Thomas Schön
NW = Niklas Wahlström

Updated  2020-03-10 22:06:28 by Johan Wågberg.