Machine Learning

AASS Core Course for Graduate Students, Spring-Summer 2005

Original road imageBrain and neural networkRoad image segmented by a neural network

This is the evolving homepage of the Machine Learning course for Ph.D. students at AASS, based on the contributions of the course members themselves. Please send any additions, comments or corrections to Tom Duckett:


Course Members
Seminar Series 1: Theory of Machine Learning
Seminar Series 2: Applications of Machine Learning
Additional Topics of Interest

The course consists of two seminar series, to be presented by each of the course members in turn. The first series (Theory), based on Tom Mitchell's book, is compulsory and worth 3 points to Ph.D. students for attending and presenting your chosen book chapter. The second series (Applications) consists of an optional assignment, where you present the state-of-the-art in applications of ML in a subject area of your choice, worth 2 extra points (or it can any sensible related topic, to be agreed with the course leader). Alternatively, students can do a programming assignment for 2 points. All course members are also asked to contribute to this web page, as a resource for current and future students.

Course Members

Seminar Series 1: Theory of Machine Learning

Chapter 1: Introduction

Chapter 2: Concept Learning

Chapter 3: Decision Tree Learning

Chapter 4: Artificial Neural Networks

Chapter 5: Evaluating Hypotheses

Chapter 6: Bayesian Learning

Chapter 8: Instance-Based Learning

Chapter 9: Genetic Algorithms

Chapter 10: Learning Sets of Rules

Chapter 11: Analytical Learning

Chapter 13: Reinforcement Learning

Seminar Series 2: Applications of Machine Learning

ABLE - Agent Building and Learning Environment

Robotic Mapping

Locally Weighted Learning for Control

Genetic Algorithms for Dexterous Manipulation

Machine Learning Strikes from Below: A Mining Application

Volumetric mapping. Unsupervised exploration of an unknown space.

Object Recognition using Vision and Machine Learning

Self-Organizing Maps

An Overview of Robot Learning

Additional Topics of Interest

  • Wikipedia (a good place to get an overview of almost any topic)
  • Tutorials on Machine Learning (Tom Dietterich, many great surveys, etc)
  • Journal of Machine Learning Research (a great, free, online journal of theoretical ML research) (directory of open access journals)
  • Sridhar Mahadevan. Machine Learning for Robots: A Comparison of Different Paradigms (a nice introduction to ML in mobile robotics).
  • Max Welling's Classnotes in Machine Learning (contains thorough derivations of many algorithms)
  • Ricardo Vilalta's ML course (contains alternate presentations of the book chapters)
  • Du Zhang. Applying Machine Learning Algorithms in Software Development (contains a nice table summarizing the characteristics of machine learning methods.)
  • Bruce Randall Donald's course notes on Learning
  • Bruce Randall Donald's course notes on Robotics
  • WEBSOM - Self-Organizing Maps for Internet Exploration
  • Introduction to Evolutionary Biology

  • Book

    Official Course Literature

    Tom Mitchell. Machine Learning. McGraw-Hill. 1997. ISBN 0-07-042807-7
    Book home page

    Literature for those who want to know more 

    Simon Haykin. Neural Networks: A Comprehensive Foundation. Prentice-Hall. 1998.
    Michael A. Arbib (editor). The Handbook of Brain Theory and Neural Networks. MIT Press. 1995.
    Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. MIT Press. 1998.
    David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addisson-Wesley. 1989.
    Rajesh P.N. Rao, Bruno A. Olshausen and Michael S. Lewicki (editors). Probabilistic Models of the Brain. MIT Press. 2002.

    Last updated 2005-07-18 by Tom Duckett.