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Department of Computer Science and Technology

Data Science

 

Course pages 2024–25

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
Code snippets: fitting.ipynblm.ipynb
Datasets investigated: climate.ipynbstop-and-search.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
§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