Data-driven Computer Science UoB

• Laurence Aitchison [[email protected]] (unit director)
• Majid Mirmehdi [[email protected]]

Holly Milllea | Amirhossein Dadashzadeh | Faegheh Sardari | Jonathan Munro | Vangelis Kazakos | Zhaozhen Xu

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!

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.

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

• [MIT OpenCourseWare]
• [Khan Academy]

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

• [Mathematics for Machine Learning]

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.

*** The image gif files for the Optional Playthings are in the ProblemSheets folder (see above). Equivalent Matlab code can be copy-pasted from lecture PDF.