DUM 623E - Machine Lear.in Marit.Indus.

Course Objectives

Describe the basic theory underlying machine learning.
Describe machine learning problems for different applications.
Describe machine learning algorithms along with their strengths and weaknesses.
Apply machine learning algorithms to solve problems.
Read research papers and analyze the state-of-the-art methods.

Course Description

This course covers the basic theory, algorithms, and applications of machine learning (ML). Computational systems can improve their performance with observed data using the machine learning techniques. These techniques include: learning theory (bias/variance tradeoffs; VC theory; large margins); supervised learning (discriminative/generative learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); ensemble learning (bagging, boosting). Lectures will discuss general issues in these topics and well-established algorithms. ML algorithms are generally applied to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining.