COMP61011 : Foundations of Machine Learning

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Maintained by G.Brown
The material for the course can be found entirely in the course textbook.

The course is taught on WEDNESDAYS.

The lab sessions are 9am-12pm, in Kilburn Building.
You are assigned to a lab group dependent on your FAMILY NAME.
If your family name starts with a letter A-R, you are in room 2.25, upstairs.
If your family name starts with a letter S-Z, you are in room 1.8, on the middle floor.
The lectures are 1pm-5pm, in the Schuster Building
Weeks 1-3 are in the Bragg Theatre
Weeks 4-5 are in the Blackett Theatre

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:
0900-1200: Lab and Discussion of previous week's material, with demonstrators present.
1200-1300: Lunch
1300-1330: Unassessed multiple choice quiz on the previous week.
1330-1700: New lecture material.
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 25th Sep) Ch 1+2Ch 3 Linear models
Part 1: PPT, PDF
Part 2: PPT, PDF
2 (Wed 2nd Oct) Ch 4Ch 5 Support Vector Machines
Part 1: PPT, PDF
Part 2: PPT, PDF
3 (Wed 9th Oct) Ch 6Ch 7 Intro to Neural Nets
Part 1 (PPTX), Part 2 (PPTX)
Decision Trees
PPT, PDF
4 (Wed 16th 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 23rd Oct) Ch 9+10Project Ensemble Methods: PPT, PDF
Feature Selection PPT, PDF

6 (Wed 30th Oct) - Project deadline.
1st Nov (FRIDAY) 4pm.
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