COMP61011 : Foundations of Machine Learning



Lab sessions


Additional resources

Maintained by G.Brown
The material for the course can be found entirely in the course textbook.
You will be provided with a printed copy of this on day 1 of the course.

The course is taught on WEDNESDAYS, 9am-5pm in room 2.19. The topics to be covered are:
Week 1: Supervised learning, linear models, gradient descent, experimental methods.
Week 2: Support Vector Machines, Nearest Neighbour methods.
Week 3: Decision Trees, ROC analysis.
Week 4: Probabilistic models, Bayes Theorem.
Week 5: Ensemble Methods, Feature Selection.
Week 6: Independent project work

The structure of each week's session will be approximately:
0900-0930: Discussion of previous week's material, with demonstrators present.
0930-1000: Unassessed multiple choice quiz on the previous week.
1000-1230: New lecture material (with appropriate breaks)
1230-1330: Lunch
1330-1700: Lab session (with demonstrators present)
The course text (provided in class) should be your main reference material. Associated with each week is a reading assignment. The unassessed MCQ will test this material, as well as the material from the previous week.

Week Taught in-classHomework Slides
1 (Wed 26th Sep) Ch 1+2Ch 3 Linear models
Part 1: PPT, PDF
Part 2: PPT, PDF
2 (Wed 3rd Oct) Ch 4Ch 5 Support Vector Machines
Part 1: PPT, PDF
Part 2: PPT, PDF
3 (Wed 10th Oct) Ch 6Ch 7 Decision Trees
4 (Wed 17th Oct) Ch 8Project (feedback opportunity in lab) Probabilistic Models
Part 1: PPT, PDF
Part 2: PPT, PDF

Now see also the Bayes Net tool developed by one of my former students!
5 (Wed 24th Oct) Ch 9+10Project Ensemble Methods: PPT, PDF
Feature Selection PPT, PDF

6 (Wed 31st Oct) - Project deadline.
2nd Nov (FRIDAY) 4pm.