Course pages 2018–19
Foundations of Data Science
Lecture notes and example sheets
- Full notes [pdf]
Sections marked * are non-examinable, but they may be helpful background reading for the example sheets. Material in the example sheets is examinable. - Example sheet 0 [pdf]: review of IA Maths for NST (not intended for supervision)
solutions [pdf] - Example sheet 1 [pdf]: probability and random variables
practical 1 [pdf] - Example sheet 2 [pdf]: inference
hint sheet [pdf]
mock exam questions [pdf]; solution 1 [pdf], solution 2, solution 3 [pdf]
practical 2 [pdf] - Example sheet 3 [pdf]: model crafting
hint sheet [pdf]
mock exam questions [pdf]; solution 4, solution 5 with supervisors
practical 3 [pdf] with hint sheet for q1 [pdf]
Errata
When I make mistakes during lectures, I upload the corrected slides to Moodle. For errors in the lecture notes, here is the list of errors discovered so far.
- Section 1.2 page 3 exercise 1.1. The display equations for P(X≤x) should have x outside the summation, not k. (Thanks to LAM.)
- Section 1.3 page 5 example 1.6. The first bullet point should have Z=2, not Z=4.
- Section 3.1 page 34 and appendix page 104. The formula for the normalizing constant for Beta is wrong.
- Section 3.1 page 35. The integrals for the posterior predictive probability should be dθ, not dx.
- Lecture 5. In lectures, I wrote down the wrong formula for posterior mean. And I got the error probability wrong: 1-0.77 is equal to 23% not 28%.
- Example sheet 2 question 2. Should say μ~Normal(μ0,σ02) rather than Normal(μ0,σ0)2.
Slides from lectures
— on Moodle
Timetable and announcements
— on Moodle
Code snippets
— posted one by one at notebooks.azure.com