#
CS485/685 - Goals

## Objectives

Computers are traditionally programmed by listing a set of instructions
that dictate the operation of the machine step by step. As a
result, machines tend to have a predetermined and rigid
behaviour. However, in many situations it would be desirable to
endow machines with the ability to adapt and learn. This course
provides an introduction to the field of machine learning, which
studies the principles and algorithms that allow a computer to learn
new concepts from some examples. The course will cover both the
theoretical foundations of machine learning (e.g., learning theory and
Bayesian learning) as well as the design of algorithms for machine
learning.

Machine learning has recently lead to major advances in several areas
of computer science. The ability to learn new concepts from
examples is particularly useful in data mining, information retrieval,
natural language processing, computer vision, computational finance,
bioinformatics, and health informatics.
Similarly, the ability to adapt to new situations is also essential for
self managing systems and robotics. Hence, this course
should be of interest to a wide audience.

## Outline

The topics we will cover include:

- Introduction to Machine Learning

- Theoretical Foundation

- Learning theory

- Bayesian learning

- Classification
- Regression
- Specific models/algorithms
- Nearest neighbors
- Decision trees

- Linear/nonlinear separators and regression

- Neural networks
- Kernel methods

- Support vector Machines
- Sequential data modeling