# Course information for COMS 4772 Advanced Machine Learning (Fall 2013)

## Basic information

*When*: Wednesday 4:10–6:00 PM*Where*: 545 Mudd*Instructor*: Daniel Hsu

## Course description

- This course is about probabilistic and linear algebraic tools for unsupervised machine learning.
- We will study various methods used by machine learning practitioners (e.g., hashing, projection pursuit, PCA, spectral clustering) which usually fall out of the purview of supervised learning.
- The goal is to develop tools for both (i) analyzing unsupervised learning methods and (ii) developing new methods.

## Prerequisites

*Algorithms*,*calculus*,*linear algebra*, and*probability*.- This is a theory course, so mathematical maturity is essential.
- It is okay to take COMS 4771 concurrently with COMS 4772 this term.
- Please contact me if you have concerns about the prerequisites.

## Tentative topics

The actual set of topics covered in the course will have a non-trivial intersection with the following list.

*Basic probability*: Gaussian distributions, Chernoff bounds, random vectors*Random linear embeddings*: Johnson-Lindenstrauss, hashing trick, subspace embeddings, compressed sensing, projection pursuit, locality-sensitive hashing*Spectral analysis*: singular value decomposition, random matrices, covariance estimation, low-rank approximation, canonical correlation analysis*Quantization and mixture models*: optimization formulations, planted partition models, Gaussian mixture model

## Course work

Homework assignments (50%) and final project (50%).