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Department of Computer Science and Technology

Data Science


Course pages 2022–23

Data Science

Lecture notes

These printed notes have some sections marked *, which are non-examinable. 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. 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
Lecture 2
Lecture 3
Lecture 4
Prediction accuracy versus model fit (* non-examinable) nn
Lecture 5
Climate dataset challenge
Lecture 6
Lecture 7
Example sheet 1
Lecture 8
Discussion of climate dataset challenge (* non-examinable)
Lecture 9
Lecture 10
7.1 Bayesianism (16:54)
video only Mock exam question 2 and walkthrough (29:35)
Example sheet 2
Climate confidence challenge (Bayesian)
Lecture 11
Lecture 12
Lecture 13
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
Lecture 15
Lecture 16
video only Mock exam question 4 and walkthrough (16:30)