MLPR 2022 activities timeline
Links to question sheets and activities will appear throughout the Semester. The precise course content may change. You may need to refresh the page.
- Weeks 1–10: Lectures: 9am–9:50am Tue/Wed/Thu:
- Weeks 1–10: study the notes, including in-note questions (not assessed).
- Weeks 2–8: prepare for the next week’s tutorial.
- Weeks 3–9: Tutorials. (See administration page for details.)
- Weeks 3–9: assignment work as appropriate.
Week 1: 19 September –
- First class meeting: Tuesday 20 September, 9am.
- Sign up for the class Forum (hypothesis).
- w1 notes: Introduction to ML with Linear Regression
- Work through any remaining background material.
Week 2: 26 September –
- w2 notes: ML fundamentals: generalization, error bars, Gaussians
- Reminder: studying the notes includes doing the questions; don't fall behind from the start!
- Prepare for Tutorial 1 next week. (Tutorial administration information.)
Week 3: 3 October –
- w3 notes: Classification and gradient-based fitting
- Tutorial 1 (Thu or Fri).
- Tell us your assignment 1 pair preference (link not working yet). First allocation of pairs at 2pm Tuesday (4 Oct). We need your pair (or no pair) preference before you can enter answers. Please be patient while we process these.
- Start work on Assignment 1 (print). You can do it by yourself or in a pair.
- Prepare for Tutorial 2 next week.
Week 4: 10 October –
- w4 notes: Bayesian linear regression
- Tutorial 2 (Thu or Fri).
- Prepare for Tutorial 3 next week.
- Finish Assignment 1 (print).
Week 5: 17 October –
- Assignment 1 due Monday 17 October, 12 noon. (0% of final mark)
Feedback is only guaranteed for submissions made on time.
- w5 notes: Bayesian model choice and Gaussian processes
- Tutorial 3 (Thu or Fri).
- Prepare for Tutorial 4 next week.
Week 6: 24 October –
- w6 notes: More detailed models: Gaussian process kernels, more non-Gaussian regression
- Tutorial 4 (Thu or Fri).
- Prepare for Tutorial 5 next week.
- Look at Assignment 2 (print) and schedule when you'll do it in weeks 6–9. You can do it by yourself or in a pair.
- Tell us your assignment 2 pair preference (link not working yet) by 2pm Tuesday (25 Oct).
Week 7: 31 October –
Week 8: 7 November –
- w8 notes: Autoencoders, PCA, Netflix Prize
- Tutorial 6 (Thu or Fri).
- We hope you haven't forgotten Assignment 2 (print).
- Prepare for the final Tutorial 7 next week.
Week 9: 14 November –
- w9 notes: Bayesian logistic regression, Laplace approximation
- Tutorial 7 (last one).
- Finish Assignment 2 (print), if you haven't already.
Week 10: 21 November –
- Assignment 2 due Monday 21 November, 12 noon. (25% of final mark)
Late work and extensions subject to Rule 1 of the School Late Policy.
- w10 notes: Sampling-based approximate Bayesian inference, variational inference
- (No tutorial)
- Please fill out the course survey (link not working yet) by TBD December.
- Take a few days off! Then revise for the exam.
December Exam (75% of your final mark)
- We expect the exam to be between 9–21 December 2022 (source: Semester Dates). The date and time will appear in the Exam timetable as soon as it's been set. We don't have early access to the date.
- We expect the exam to be a closed-book pen-and-paper exam, held in person in Edinburgh. However this is still subject to confirmation by the University, and assuming there are no further pandemic restrictions.
- The library has past exam papers. Although you may not be able to do every question: the course has changed slowly over the years, after a big change in 2016. There is no past paper from 2020.