COMP61011 : Foundations of Machine Learning

Home

Lectures

Lab sessions

Assessment

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
PPT, PDF
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.
-