14:35

Briefing

11:23

11.7 Drift analysis

12:38

11.4-6 Stationarity and limiting behaviour

11:37

11.1, 11.2 Calculations with Markov chains

03:22

11 The behaviour of Markov chains

09:57

10.3, 10.4 Sequence models with history

04:56

10.2 Likelihood and memorylessness

12:09

10.1 Markov chains

07:00

8.3 Non-parametric sampling

23:37

8.2 Hypothesis testing and p-values

13:03

8.1 Resampling for confidence intervals

03:58

8. Frequentism

06:06

6.3 The empirical distribution

15:10

6.1, 6.2. Empirical cumulative distribution

24:46

7.2-3 Bayesian model-crafting and posteriors

06:29

7.4 Bayesian readouts

16:54

7. Bayesianism

19:25

5.2 Computational Bayes

13:21

5.1 Monte Carlo integration

09:18

4.3 Deriving the likelihood

15:38

4.2 Calculations with Bayes's rule

17:27

4.1 Bayes's rule for random variables

23:36

Exam walkthrough 1: fitting probability models

14:18

1.7 Supervised learning

08:14

1.6 Generative modelling

10:00

1.5 Likelihood notation

08:01

1.4 Numerical optimization

17:35

1.3 Maximum likelihood estimation

03:21

1.2 Standard random variables

15:20

1.1 Specifying probability models

04:08

1. Learning with probability models

18:07

DisjointSets / Union Find

29:35

Amortized analysis of the Fibonacci heap

18:13

Fibonacci heap

18:26

Binary & binomial heaps

14:17

Potential functions (for amortized analysis)

10:59

Amortized analysis

10:41

Amortization

08:56

Aggregate analysis of running time

19:38

Max-flow min-cut theorem (proof of correctness of Ford-Fulkerson)

14:16

Topological sort

14:29

Minimum spanning tree (Prim and Kruskal algorithms)

19:38

Max-flow min-cut theorem (proof of correctness of Ford-Fulkerson) [old]

21:55

Ford-Fulkerson algorithm for finding max flow

09:31

Flow networks

11:01

Matching algorithms

09:29

Algorithms and proofs

15:25

Dijkstra's algorithm

11:37

Depth-first search

13:43

Johnson's algorithm (all-to-all shortest paths)

13:06

Dynamic programming and shortest paths on graphs

24:01

Dijkstra's algorithm: proof of correctness

12:13

Bellman-Ford algorithm (shortest paths on graphs with negative weights)

06:43

Breadth-first search and shortest paths on graphs

14:27

Graphs

08:14

Tick 2: COVID data science exercise, including tracking the R number

06:37

Tick 1: COVID simulator using numpy

07:41

Underfitting and overfitting

15:15

Autoencoder in maths

28:37

Autoencoders in practice