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Department of Computer Science and Technology

Data Science


Course pages 2023–24

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.

Example sheet 0 and solutions
§1–§4. Learning with probability models
Lecture 1
Lecture 2
Lecture 3
3.1, 3.2 Prediction accuracy versus probability modelling (* non-examinable)
Lecture 4
3.3 Neural networks (* non-examinable)
Lecture 5
Lecture 6
Lecture 7
Code snippets: fitting.ipynblm.ipynb
Datasets investigated: climate.ipynbstop-and-search.ipynb
§5, §6, §8. Bayesian inference and Monte Carlo
Lecture 8
Lecture 9
Discussion of climate dataset challenge (* non-examinable)
4.1 Measuring model fit (* non-examinable)
Lecture 10
Lecture 11
video only Mock exam question 2 and walkthrough (29:35)
Code snippets: bayes.ipynb
§7, §9, §10. Frequentist methods: hypothesis testing and empirical evaluation
Lecture 11 ctd
Lecture 12
Lecture 13
Lecture 14
9.3 Hypothesis testing (continued)
10. Holdout evaluation and the challenge of induction (* non-examinable)
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
Lecture 16
Example sheet 4
OPTIONAL ex4 practical exercises [ex4.ipynb] (for supervisions)
OPTIONAL Stoat-finding challenge and Moodle submission (deadline noon 2023-12-24)