#	Lecture	Lecturer	Preparation exercise	Read	Watch more	Slides	Recording from 2022
F1	Introduction and math preliminaries	JS		1, 2.1	3 min 5 min	PDF	F1 (2022)
–	Introduction to statistical thinking	DZ
–	Introduction to Python		Python notebook Solutions Google Colab				F0 (2022)
F2	Basic regression, linear regression	AHR	Python notebook Google Colab	3.1, 3.A, 3.3, 5.3		PDF Example	F2 (2022)
F3	Basic classification, logistic regression	AHR	Python notebook Google Colab	3.2, 4.5, (5.1)	15 min	PDF Example	F3 (2022)
F4	Classification cont'd, discriminant analysis, and nearest neighbours	SH	Python notebook Google Colab	10.1, 2.2	k-NN demo blackboard notes	PDF	F4 (2022)
F5	Generalization performance	SH	Python notebook Google Colab	4.1-4.4	12 min 14 min Video addendum to F5.mp4 blackboard notes	PDF	F5 (2022)
F6	Tree-based methods, bagging, boosting	JC	Python notebook Google Colab	2.3, 7.1-7.3, (7.4)	10 min	PDF	F6 (2022)
F7	Deep neural networks	JS	Python notebook Google Colab	6.1, "Logistic regression for more than two classes" in 3.2, 6.2-6.3 (skip "Backpropagation" in 6.2)	NN playground 19 min 5 min	PDF	F7 (2022)
F8	Convolutional neural networks and numerical optimization	JS	Python notebook Google Colab	6.3, 5.2, 5.4-5.5	3 min 3 min 21 min	PDF blackboard notes	F8 (2022)
F9	Ethics & limitations & new directions for supervised learning	SH	-	12	blackboard notes
F10	Summary and guest lecture on "ML in practice: from protons to electron"	JS				PDF

JS = Jens Sjölund
AHR = Antonio Horta Ribeiro
SH = Sara Hamis
DZ = Dave Zachariah
JC = Jodie Cochrane