The course comprises 10 lectures for 1RT705 and additional 4 lectures for 1RT003.

	Lecture	Lecturer	Reading	Slides	Recording and discussion forum	Recording from last year
1.	Introduction, probabilistic modelling	NW	[B] 1.1-1.2.3, 8.2-8.2.1 (one page, not including 8.2.1)	pdf	Lecture 1	L1 -2020
2.	Conjugate prior, Binomial-Beta conjugate pair, Multivariate Gaussian distribution	NW	[B] 2.1, 2.3.1-2.3.6, 2.4, [LWLS] 9.A, Formula sheet for the Gaussian distribution	pdf	Lecture 2	Last part of L1 above and parts of L2 below
3.	Bayesian linear regression, marginal likelihood	NW	[B] 3.3-3.4, [LWLS] 9.1-9.2	pdf	Lecture 3	L2 -2020
4.	Gaussian processes I (only 1RT003)	AR	[LWLS] 9.3, [B] 6.4.1, 6.4.2	pdf	Lecture 4	L7 -2020
5.	Gaussian processes II (only 1RT003)	AR		pdf	Lecture 5	L8 -2020
6.	Bayesian graphical models	AR	[B] 8, 8.1, 8.2	pdf	Lecture 6	L3 -2020
7.	Monte Carlo methods	JS	[B] 11, 11.1.4, 11.2-11.3	pdf	Lecture 7	L4 -2020
8.	Markov Random Fields, Factor Graphs	NW	[B] 8.3 - 8.3.3, 8.4.3	pdf	Lecture 8	L5 -2020
9.	Message passing	NW	[B] 8.4-8.4.4, 10.7.2*	pdf	Lecture 9	Last part of L5 above and factor graph example in L6 below.
10.	Variational inference (only 1RT003)	JS	[B] 1.6.1, 10-10.1.1, 10.3-10.3.1	pdf	Lecture 10	L6 -2020
11.	Unsupervised learning	TS	[B] 2.5, 12.1, 12.2 (but not 12.2.1-4)	pdf	Lecture 11	L9 -2020
12.	Semi-supervised learning and generative models	TS	[LWLS] 10.1, 10.3	pdf	Lecture 12	New this year
13.	Variational autoencoder (only part of 1RT003)	TS	[KW] 1-2.4	pdf	Lecture 13	L10 -2020
14.	Summary / guest lecture with Agrin Hilmkil, Storytel	NW		pdf	Lecture 14

(you need to be enrolled to the course and logged in to access the recording)

Lecture 4, 5, 10, 13 covers material which is part of the course content of 1RT003 only. Note that in the schedule the lectures are numbered according to the table above, also for 1RT705.

[B] Christopher M. Bishop, Pattern Recognition and Machine Learning.
[LWLS] Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön Machine Learning - A First Course for Engineers and Scientists
[KW] Diederik Kingma and Max Welling, An introduction to Variational Autoencoders

NW = Niklas Wahlström

JS = Jens Sjölund

AR = Antonio Ribeiro

TS = Thomas Schön

Note that recorded lectures from previous year covers the material in different order and there might admin info in these which might be obsolete.

* = Bishop explain approximate inference (using Expectation propagation) in graphs in a quite complicated manner. I will give a (hopefully) more intuitive version in the lecture.