Data Science
Lecture notes
 Abridged notes as printed — examinable material only
 Extended notes with extra material on nonexaminable material such as neural networks
Announcements and Q&A
— MoodleLecture schedule
This is the planned lecture schedule. It will be updated as and when actual lectures deviate from schedule. Material marked * is nonexaminable. Slides are uploaded the night before a lecture, and reuploaded after the lecture with annotations made during the lecture.
Prerequisites  
Example sheet 0 and solutions  
§1–§4. Learning with probability models  
Lecture 1  
Lecture 2 
1.4 Numerical optimization (8:01)

Lecture 3 
1.5 Likelihood notation (10:00)
1.6 Generative models (8:14)
1.7 Supervised learning (14:18)
3.1, 3.2 Prediction accuracy versus probability modelling (* nonexaminable)

Lecture 4 
Mock exam question 1
and walkthrough (23:36)
3.3 Neural networks (* nonexaminable)

Lecture 5 
2.1 Linear modelling (13:27)
2.2 Feature design (19:39)

Lecture 6 
2.6 Interpreting parameters (20:03)

Lecture 7  
OPTIONAL ex1 practical exercises
[ex1.ipynb] (for supervisions)
OPTIONAL PyTorch introduction and challenge OPTIONAL climate dataset challenge climate.ipynb 

§5, §6, §8. Bayesian inference and Monte Carlo  
Lecture 8 
5.2 Bayes's rule calculations (15:37)

Lecture 9 
6.1 Monte Carlo integration (13:21)
Discussion of climate dataset challenge (* nonexaminable)
4.1 Measuring model fit (* nonexaminable)

Lecture 10 
6.2 Bayes's rule via computation (19:25)
8.3 Finding the posterior (24:45)

Lecture 11 
8.1, 8.2 Bayesianism (16:54)
8.4 Bayesian readouts (6:28)

video only  Mock exam question 2 and walkthrough (29:35) 
OPTIONAL ex2 practical exercises
[ex2.ipynb] (for supervisions)
OPTIONAL Climate confidence challenge
Code snippets: bayes.ipynb


§7, §9, §10. Frequentist methods: hypothesis testing and empirical evaluation  
Lecture 11 ctd 
5.3 Deriving the likelihood (9:17)

Lecture 12 
7.1–7.2 Empirical cdf (15:10)
9. Frequentism (3:58)

Lecture 13 
9.3 Hypothesis testing (23:37)

Lecture 14 
9.3 Hypothesis testing (continued)
10. Holdout evaluation and the challenge of induction (* nonexaminable)

video only  Mock exam question 3 and walkthrough (18:20) 
Example sheet 3
OPTIONAL ex3 practical exercises [ex3.ipynb] (for supervisions) OPTIONAL Climate confidence challenge 

§11+§12. Markov chains and other sequence models  
Lecture 14 ctd  
Lecture 15 
12.1 Learning a Markov chain (4:56)
11, 11.1 Markov chain models (12:09)

Lecture 16 
Markov chain behaviour (3:22)

Example sheet 4
OPTIONAL ex4 practical exercises [ex4.ipynb] (for supervisions) OPTIONAL Stoatfinding challenge and Moodle submission (deadline noon 20231224) 