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2010:ml

Introduction to Machine Learning

The study of learning from data is commercially and scientifically important. This one month short course is designed to give first year Ph.D. students a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics.
Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

Schedule

Topic Date Slides note
Introduction 2010.03.04 Introduction =>
What is Machine Learning
Point estimation
Supervised Learning 2010.03.11 Concept learning =>
Decision tree
Naive Bayes classifier
Support Vector Machines
Boosting
Unsupervised Learning 2010.03.18 Dimension Reduction =>
Why AddBoost?
Clustering
Reinforcement Learning 2010.03.25 Clustering (cont.) =>
Hiden Markov Model
Kalman Filter
course talk 2010.04.01 Robust PCA By Zhengfang Hu

Text books

Reference website

2010/ml.txt · Last modified: 2014/05/22 08:34 (external edit)