Introduction to Statistical Machine Learning 2008 (ANU COMP6467/4670)
as part of the Masters of Information & Communications Technology (MICT) program
Course Coordinators
Presenters
Tutor
Time and Place
- Lecture: Thursday 2pm-5pm, Building 33 CHEM T2
- Tutorial: Friday 10am-noon, Building 31 Ian Ross R221 Graduate Teaching Room
Enrollment
Prerequisites: some background in elementary statistics and probabilities, numerical algorithms, and programming experience.
If you fulfil official requirements, please send an email to Scott Sanner (firstname.lastname@nicta.com.au) that he can support your enrolment.
Assessment
- 2 written assignments (15% each)
- written examination (70%)
Important dates and special announcements
- Assignment 1, Fisher Iris Data, due: April 30, 23:59
- Assignment 2, HTML Data, due: June 4, 23:59
- Final exam: TBA
Contact hours for students
After the lecture and per email
Textbook
Required:
- Christopher M. Bishop: Pattern Recognition and Machine, Springer
We also recommend (ordered by priority):
- 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
| Week | Date | Presenter | Content |
|---|---|---|---|
| 1 | Feb 28 | Marcus | Introduction (Intro Slides) |
| 2 | Mar 6 | Peter | Preparation: Math., Stat. and Optimization (Linear Algebra, Optimization, Probability) |
| 3 | Mar 13 | Peter | Regression (Regr Slides), Classification (Class Slides), Cross Validation (XVal Slides), (Tutorial 1 Notes) |
| 4 | Mar 20 | Wray | K-Means, PCA/ICA (KMeans / PCA Slides) (Additional PCA) |
| 5 | Mar 27 | Scott | Artificial Neural Networks (ANN Slides) (Review Slides) |
| 6 | Apr 3 | Vishy | Exponential Family (Tutorial 3 Notes) (Sarah's Scribe Notes I) (Vishy's Notes) |
| 7 | Apr 10 | Vishy | Kernels and SVMs (Tutorial 4 Notes) (Sarah's Scribe Notes II) (Kernel & SVM Reference) |
| 8 | Apr 17 | none | none |
| 9 | Apr 24 | none | none |
| 10 | May 1 | Tiberio | Graphical Models |
| 11 | May 8 | Tiberio | Mixture models and EM (Graphical Models & EM) |
| 12 | May 15 | Wray | Text/natural language (Machine Learning for Text) |
| 13 | May 22 | Scott | Sequential Prediction and HMMs (Sequential Prediction Slides) |
| 14 | May 29 | Marcus | Foundations (Foundation Slides) |
| 15 | Jun 5 | Scott | Discussion (SML Final Review) |
For excellent supplemental slides, see Andrew Moore's Tutorials.
Last modified 2008-06-05 11:58 PM