Introduction to Statistical Machine Learning (COMP6467/COMP4670)

as part of the Masters of Information & Communications Technology (MICT) program

Course Coordinators

Simon Guenter
Marcus Hutter

Presenters

Simon Guenter
Marcus Hutter
Nic Schraudolph
Tiberio Caetano
SVN Vishwanathan
Alex Smola
Douglas Aberdeen
Brian Parker

Tutor

Qinfeng (Javen) Shi

Time and Place

Assessment

  • 2 written assignments (30% each)
  • No offical tutorial lessons; special tutorial mini-sessions during normal course time if needed (will be announced)
  • oral examination (40%)

Important dates and special announcements

  • 9.5: 1. Assignment due
  • 30.5: 2. Assignment due
  • 6.6: Oral exam

    • Contact hours for students

    After the lecture and per email

    Textbook

    There is no textbook per se, but if you want to delve deeper into this topic, we recommend (ordered by priority):

    • Christopher M. Bishop: Pattern Recognition and Machine, Springer
    • Hastie, Tibshirani, and Friedman: The Elements of Statistical Learning, Springer
    • Hutter, Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability, Springer

    Overview

    This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.)

    Syllabus

    Nr Date Presenter Content
    1 21.2 S./M. Administrative + Overview (overview slides)
    o 28.3   No course (ANU closed because of storm damage)
    2 7.3 Marcus Foundations (Bayesian statistics, induction, sequence prediction, paradoxes, meaning of probability) (slides2)
    3 14.3 Simon Regression + Classification (slides3.1 , slides3.2)
    4 21.3 Simon Unsupervised learning (clustering, PCA, ICA) (slides4)
    5 28.3 Simon bias/variance, overfitting, regularization, validation (slides5)
    A     first assignment available (Fisher Iris Data, Assignment 1)
    6 4.4 Nic density estimation (semi-/non-/parametric models, EM) (slides6). Read also: sections 1 and 2 of this EM tutorial.)
    7 2.5 Brian bioinformatics (slides7)
    A     second assignment available (Assignment 2)
    8 9.5 Doug Reinforcement learning (slides8)
    D     first assignment due
    9 16.5 Vishy Kernel methods and Support Vector Machines (slides9)
    10 23.5 Tiberio Graphical Models (Link to Graphical Models slides)
    11 30.5 Javen discussion assignments
    D     second assignment due
    OE 6.6 S./M. oral examination

    Last modified 2008-01-24 11:02 PM