Data Science

Lecture notes

• Full notes as printed, including example sheets
• Corrections

These printed notes have some sections marked *, which are non-examinable. If you spot a mistake in the printed notes, let me know.

Lecture schedule

This is the planned lecture schedule. It will be updated as and when actual lectures deviate from schedule. Links are to prerecorded videos. Slides are uploaded the night before a lecture, and re-uploaded after the lecture with annotations made during the lecture.

	Example sheet 0 (prerequisites) also included as an appendix in the notes, and solutions
Lecture 1 [slides]	1. Learning with probability models (4:08) fitting, climate 1.1 Specifying probability models (15:20) fitting 1.2 Standard random variables (3:21)
Lecture 2 [slides]	1.3 Maximum likelihood estimation (17:35) fitting 1.4 Numerical optimization (8:01)
Lecture 3 [slides]	1.5 Likelihood notation (10:00) 1.6, 1.8 Generative models (8:14) fitting 1.7 Supervised learning (14:18) fitting
Lecture 4 [slides]	Prediction accuracy versus model fit (* non-examinable) nn Mock exam question 1 and walkthrough (23:36)
Lecture 5 [slides]	2.1 Linear modelling (13:27) lm 2.2 Feature design (19:39) 2.3 Diagnosing a linear model (5:29)
	Climate dataset challenge
Lecture 6 [slides]	2.5 Linear algebra and least squares (12:07) 2.6 Interpreting parameters (20:03)
Lecture 7 [slides]	2.4 Probabilistic linear modelling (9:45) 4.1 Bayes's rule for random variables (17:27)
	Example sheet 1
Lecture 8 [slides]	4.2 Bayes's rule calculations (15:37) Discussion of climate dataset challenge (* non-examinable)
Lecture 9 [slides]	5.1 Monte Carlo integration (13:21) 5.2 Bayes's rule via computation (19:25) bayes.ipynb
Lecture 10 [slides]	7.1 Bayesianism (16:54) 7.2 Finding the posterior (24:45) 7.3 Bayesian readouts (6:28)
video only	Mock exam question 2 and walkthrough (29:35)
	Example sheet 2 Climate confidence challenge (Bayesian)
Lecture 11 [slides]	4.3 Deriving the likelihood (9:17) 6.1–6.2 Empirical cdf (15:10) 6.3 The empirical distribution (6:06)
Lecture 12 [slides]	8. Frequentism (3:58) 8.1, 8.2 Resampling / confidence intervals (13:03) freq.ipynb 8.6 Non-parametric resampling (7:00)
Lecture 13 [slides]	8.3 Hypothesis testing (23:37) The problem of induction (* non-examinable)
video only	Mock exam question 3 and walkthrough (18:20)
	Example sheet 3 Climate confidence challenge (Frequentist)
Lecture 14 [slides]	10, 10.1 Markov chain models (12:09) markov.ipynb Markov chain behaviour (3:22) 10.2–3 Calculations with Markov chains (11:37)
Lecture 15 [slides]	10.4 Stationarity, 10.5* Average behaviour (12:38) 10.6 Drift analysis (11:23)
Lecture 16 [slides]	11.1 Likelihood and memorylessness (4:56) 11.2–3 Sequence models with history (9:57)
video only	Mock exam question 4 and walkthrough (16:30)
	Example sheet 4 Stoat-finding challenge