Data Science
Lecture notes
- Abridged notes as printed — examinable material only
- Extended notes with extra material on non-examinable material such as neural networks
If you spot a mistake in the printed notes, let me know.
Announcements and Q&A
Lecture schedule
This is the planned lecture schedule. It will be updated as and when actual lectures deviate from schedule. Material marked * is non-examinable. Slides are uploaded the night before a lecture, and re-uploaded after the lecture with annotations made during the lecture.
Pre-recorded versions of each lecture are available on last year's version of this page.
Prerequisites | |
Example sheet 0 and solutions | |
§1–§4. Learning with probability models | |
Lecture 1 |
1. Learning with probability models
1.1 Specifying probability models
|
Lecture 2 |
1.2 Standard random variables
1.3 Maximum likelihood estimation
1.4 Numerical optimization
|
Lecture 3 |
1.5 Likelihood notation
1.6 Generative models
1.7 Supervised learning
3.1, 3.2 Prediction accuracy versus probability modelling (* non-examinable)
|
Lecture 4 |
Mock exam question and walkthrough
3.3 Neural networks (* non-examinable)
|
Lecture 5 |
2.1 Linear modelling
2.2 Feature design
2.3 Diagnosing a linear model
|
Lecture 6 |
2.5 The geometry of linear models
2.6 Interpreting parameters
|
Lecture 7 |
2.4 Probabilistic linear modelling
|
Example sheet 1 Example sheet
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 |
5.1 Bayes's rule for random variables
5.2 Bayes's rule calculations
|
Lecture 9 |
5.2 Bayes's rule calculations (continued)
6.1 Monte Carlo integration
4.1 Measuring model fit (* non-examinable)
|
Lecture 10 |
6.2 Bayes's rule via computation
8.3 Finding the posterior
|
Lecture 11 |
8.1, 8.2 Bayesianism
8.4 Bayesian readouts
|
video only | Mock exam question 2 and walkthrough |
OPTIONAL ex2 practical exercises
[ex2.ipynb] (for supervisions)
OPTIONAL Climate confidence challenge |
|
§7, §9, §10. Frequentist methods: hypothesis testing and empirical evaluation | |
Lecture 11 ctd |
5.3 Deriving the likelihood
|
Lecture 12 |
7.1–7.2 Empirical cdf
7.3 The empirical distribution
9. Frequentism
9.1, 9.2 Resampling / confidence intervals
9.6 Non-parametric resampling
|
Lecture 13 |
9.3 Hypothesis testing
|
Lecture 14 |
9.3 Hypothesis testing (continued)
10. Holdout evaluation and the challenge of induction (* non-examinable)
12.2–3 Sequence models with history: RNNs and Transformers
|
video only | Mock exam question 3 and walkthrough |
Example sheet 3
OPTIONAL ex3 practical exercises [ex3.ipynb] (for supervisions) OPTIONAL Climate confidence challenge |
|
§11+§12. Markov chains and other sequence models | |
Lecture 15 |
12.1 Learning a Markov chain
11, 11.1 Markov chain models
11.2–3 Calculations with Markov chains
|
Lecture 16 |
Markov chain behaviour
11.4 Stationarity, 11.5* Average behaviour
|
video only | Mock exam question 4 and walkthrough |
Example sheet 4
OPTIONAL ex4 practical exercises [ex4.ipynb] (for supervisions) OPTIONAL Stoat-finding challenge and Moodle submission |