See the table below for which sections of the coursebook to read each week.

	Lecture	Lecturer	Reading	Warm-up videos	Slides	Recording and discussion forum
1.	Introduction and math basics	JS	1, 2.1	3 min 5 min	pdf	Lecture 1
-	Introduction to Python	JS	Python notebook Google Colab	Python crash course	pdf	Python lecture
2.	Linear regression and regularization	TS	3.1, 5.3, 3.A	9 min 1 min	pdf notes	Lecture 2
3.	Logistic regression	TS	3.2, 3.3, 4.5	3 min 15 min	pdf notes	Lecture 3
4.	LDA, QDA, k-NN	SM	10.1, 2.2	15 min 5 min	pdf notes	Lecture 4
5.	Bias-variance tradeoff, cross-validation	SM	4.1-4.4	6 min 3 min	pdf notes	Lecture 5
6.	Tree-based methods, bagging	JS	2.3, 7.1, 7.2	10 min 3 min	pdf	Lecture 6
7.	Boosting	JS	7.3, (5.2), (7.4)	2 min 5 min	pdf notes	Lecture 7
8.	Deep learning part 1	NW	6.1, "Logistic regression for more than two classes" in 3.2	19 min	pdf notes demo	Lecture 8
9.	Deep learning part 2	NW	6.2-6.3, 5.4-5.5, (skip "Backpropagation" in 6.2)	21 min	pdf notes	Lecture 9
10.	Summary and guest lecture	JS			pdf	Lecture 10

JS = Jens Sjölund
TS = Thomas Schön
SM = Sebastian Mair
NW = Niklas Wahlström