Lecture 1: Sept 1

Course Overview

Overview of unsupervised learning and the structure of the course.

Note: Unsupervised learning is a wide and rapidly developing field that it is impossible to cover all aspects in this course. E.g., we are not able to cover neural network based methods (will cover in the course "Statistical Deep Learning" in HT27), information theory based methods (will cover in the course "Statistical Information Theory"), fuzzy clustering, community detection in empirical networks, topological based methods, practical issues to handle large dataset, extensive discussion of validation methods for dimensionality reduction, generative models, etc. If you are interested in those advance topics, you can contact me (Chun) in person.

Basic Concepts in Clustering

a) Similarity and Dissimilarity measures

Reading: Sec 14.3.1-3 in ESL

b) Loss function for clustering

Reading: Sec 14.3.4-5 in ESL

c) k-mean

Reading: Sec 9.1 in PRML