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Data Science

Lecture notes

Abridged notes for sections 1–3 — These cover all examinable material, though they go into more depth and have more examples than lectures. They also include a non-examinable section on neural networks.
Notes for section 4
Draft manuscript of a book based on this lecture course

If you spot a mistake in the printed notes, let me know.

Announcements and Q&A

— Moodle

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.

Prerequisites
	Example sheet 0 and solutions
§1–§4. Learning with probability models
Lecture 1 [slides]	1. Learning with probability models 1.1 Specifying probability models
Lecture 2 [slides]	1.2 Standard random variables 1.3 Maximum likelihood estimation 1.4 Numerical optimization with scipy
Lecture 3 [slides]	1.5 Likelihood notation 1.6 Types of model 1.7 Supervised and unsupervised learning
Lecture 4 [slides]	Mock exam question 1 3. Neural networks as probability models (* non-examinable)
Lecture 5 [slides]	2.1 Linear modelling 2.2 Feature design
Lecture 6 [slides]	2.3 Diagnosing a linear model 2.5 The geometry of linear models
Lecture 7 [slides]	2.6 Interpreting parameters
Lecture 8 [slides]	2.4 Probabilistic linear modelling Discussion of climate dataset challenge (* non-examinable)
	Code snippets: fitting.ipynb and lm.ipynb Example sheet 1 OPTIONAL ex1 practical exercises [ex1.ipynb] (for supervisions) OPTIONAL PyTorch introduction and challenge OPTIONAL climate dataset challenge climate.ipynb Datasets investigated: climate.ipynb, stop-and-search.ipynb
§5, §6, §8. Bayesian inference and Monte Carlo
Lecture 8 ctd. [slides]	8. Bayesianism
Lecture 9 [slides]	5.1 Bayes's rule for random variables 5.2, 8.3 Bayes's rule calculations 6.1 Monte Carlo integration
Lecture 10 [slides]	8.1, 8.2 Bayesianism 8.4 Bayesian readouts 6.2 Bayes's rule via computation
Lecture 11 [slides]	4, 10. Generalization, model choice, and holdout sets 8.7 Bayesian model choice
video only	Mock exam question 2 and walkthrough (29:35)
	Example sheet 2 OPTIONAL ex2 practical exercises [ex2.ipynb] (for supervisions) OPTIONAL Climate confidence challenge Code snippets: bayes.ipynb
§7, §9, §10. Frequentist inference and empirical distributions
Lecture 11 ctd	5.3 Deriving the likelihood
Lecture 12 [slides]	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 [slides]	9.3 Hypothesis testing
Lecture 14 [slides]	Approaches to generalization: summary
video only	Mock exam question 3 and walkthrough (18:20)
	Code snippets: freq.ipynb Example sheet 3 ex3 practical exercises [ex3.ipynb] OPTIONAL Climate confidence challenge
§11+§14. Random systems
Lecture 14 ctd [slides]	12.2 Markov models for text sequences 12.2 RNNs and Transformers (* non-examinable)
Lecture 15 [slides]	RLHF (* non-examinable) 12.1 Learning Markov models 11.2 Calculations with causal diagrams
Lecture 16 [slides]	12.3 HMMs 11.4 Stationarity and average behaviour of Markov chains
	Example sheet 4 OPTIONAL ex4 practical exercises [ex4.ipynb] (for supervisions) OPTIONAL Stoat-finding challenge and Moodle submission (deadline Christmas eve)