540 -Machine Learning

Time & Place: T R 2:00-3:30 (DEMP 201).

Start date: January 11.

Office hours: Tuesday 12-1:00pm and 3:30-4:30pm (ICICS 146).

TA: Mirela Andronescu, andrones at cs etc.

Office hours: Wednesday 10am-11am (ICICS 191).


Machine Learning is, to a large extent, the process of acquiring abstractions of the real world from a sparse set of observations. The observations can include software, webpages, DNA and protein arrays, motion capture data, images, computer game logs, music, video, controlled simulations and so on. Thus ML is about letting computers infer models of the world and ways of acting, as opposed to us telling them what to do precisely through excruciating programming. This course will also tackle some of the fundamental problems at the interface of learning, decision theory and probability. The course develops the theoretical foundations, representations and algorithms for active learning, value of information problems, experimental design, attention, optimal control and reinforcement learning. The course will present these developments in the the context of web crawling, relevance feedback in HCI, robotic exploration, question answering systems, clinical trials, active vision, active labelling of database entries, network problems, graphics and animation and optimal control.

The pre-requisites are linear algebra, calculus and basic statistics or probability. I you don't have these skills, you have two weeks to acquire them before the course starts. I can provide background handouts if you come to my office early enough.



  • Assignments: 20%
  • Exam: 30% (April 5)
  • Project: 50% (Due April 28, but the proposal is due March 15).
  • The instructor has the right to change the marking scheme under reasonable and acceptable circumstances. Projects and homeworks will be penalized 10% per day of being late.


    Recommended books

  • Pattern Recognition and Machine Learning
    by Christopher Bishop.
  • Gaussian Processes for Machine Learning
    by Chris Williams.
  • Artificial Intelligence: A Modern Aproach
    by Stuart Russell and Peter Norvig.


    Recommended websites

  • Gaussian process website
  • Andrew Ng
  • Michael Jordan
  • Carlos Guestrin
  • Satinder Singh
  • Stefan Schaal
  • Jan Peters

    nando at cs dot ubc dot you know my country
    ICICS 183
    Tel 8226770