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
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