COMS20011_2020

Data-driven Computer Science UoB

Staff

Laurence Aitchison [laurence.aitchison@bristol.ac.uk] (unit director)
Majid Mirmehdi [m.mirmehdi@bristol.ac.uk]

Teaching Assistants

Structure

Lecture videos for a week will be released on Monday and posted here. Please take a look at them promptly!

The coursework will be 40% of the mark and the exam will be 60% (deadlines TBA).

TA led sessions on Thursday 3-5 are the main route for feedback on all aspects of the course: lectures, labs and coursework. These will start on 11th Feb, and you will be assigned a group by email before then. They will be hosted through the public Teams group [grp-COMS20011_2020] (if the link doesn't work, just search for "grp-COMS20011_2020").

There are lecturer-led Q&A sessions on Mondays at 4pm. The first of these (Feb 1st) will be TA led, to help getting IT set up for labs.

There will be "lab" exercises released in the "lab" folder. Please do them promptly and bring any questions to the TA-led sessions: the coursework is heavily based on the labs!

Mathematical background material

Important: these are not pre-requisites! Please don't try to look at all of the material! They're intended as supplements to the first-year maths courses to help clear up specific issues with the derivations in the course. Feel free to raise an issue/pull-request if you have recommendations for other resources.

Probability and statistics

A Modern Introduction to Probability and Statistics, Understanding Why and How (Dekking et al.)
[MIT OpenCourseWare]
[Khan Academy] (from Probability)

Calculus:

Linear Algebra:

Linear Algebra for Everyone (Gilbert Strang)
[Khan Academy]

All of the above

[Mathematics for Machine Learning]

Weekly lecture material

Week 13: 01/02/2021 (Majid)

Data Acquisition & Pre-processing

Lecture	video	slides
1. Intro to COMS20111 - very fishy	[Stream link]	[pdf]
2. Intro - Part 2 - example projects	[Stream link]	[pdf]
3. Data Acquisition - Sampling - Acquisition	[Stream link]	[pdf]
4. Data Characteristics - Distance Measures	[Stream link]	[pdf]
5. Data Characteristics - Covariance - Eigen Analysis - Outliers	[Stream link]	[pdf]
Problem Sheet 1 Updated - New Q 12/02/21	Self/Group study	[pdf]
Problem Sheet 1 Updated - New Q/A 12/02/21	Answers	[pdf]
Q&A Session	[Stream link]	-

(Week 14): 08/02/2021 (Laurence)

Lecture	video	slides
1. Maximum likelihood for a coin	[Stream link]	[notebook 1]
2. Bayes for a coin	[Stream link]	[notebook 1]
3. Intro to supervised learning	[Stream link]	[notebook 2]
4. Linear regression derivation	[Stream link]	[notebook 2]
Problem Sheet W14		[pdf]
Problem Sheet W14 Solution Explanation		[pdf]
Q&A Session	[Stream link]	-

(Week 15): 14/02/2021 (Laurence)

Lecture	video	slides
1. Linear regression examples	[Stream link]	[notebook 2]
2. Overfitting	[Stream link]	[notebook 3]
3. Cross-validation	[Stream link]	[notebook 3]
4. Regularisation	[Stream link]	[notebook 3]
Problem Sheet W15		[notebook]
Problem Sheet W15 Solution Explanation		[pdf]
Q&A Session	[Stream link]	-

(Week 16): 14/02/2021 (Laurence)

Lecture	video	slides
1. Logits parameterisation	[Stream link]	[notebook 4]
2. Gradient descent + overfitting	[Stream link]	[notebook 4]
3. KNN/WNN and nearest centroids	[Stream link]	[notebook 4]
4. Bayesian classification	[Stream link]	[notebook 4]
Problem Sheet W16		[notebook]
Q&A Session	[Stream link]	-

(Week 18): 8/03/2021 (Laurence)

Lecture	video	slides
1. Clustering vs classification	[Stream link]	[notebook 5]
2. K-means clustering	[Stream link]	[notebook 5]
3. EM for Gaussian mixture models	[Stream link]	[notebook 5]
4. Objective for EM [Non-examinable]	[Stream link]	[notebook 5]
Problem Sheet W18		[notebook]

Notebook pdfs

I have printed the Notebooks as pdfs. Note that this really doesn't work well, as many of the interactive plots can't be printed.

Notebook
[notebook 1]
[notebook 2]
[notebook 3]
[notebook 4]

sjpollard/COMS20011_2020