CS489/698 Winter 2018 - Introduction to Machine Learning

Course Description:

The course introduces students to the design of algorithms that enable machines to "learn". In contrast to the classic paradigm where machines are programmed by specifying a set of instructions that dictate what exactly a machine should do, a new paradigm is developed whereby machines are presented with examples from which they learn what to do. This is especially useful in complex tasks such as natural language processing, information retrieval, data mining, computer vision and robotics where it is not practical for a programmer to enumerate all possible situations in order to specify suitable instructions for all situations. Instead, a machine is fed with large datasets of examples from which it automatically learns suitable rules to follow. The course will introduce the basics of machine learning and data analysis.

Course Objectives:

At the end of the course, students should have the ability to:

• Formalize a task as a machine learning problem
• Identify suitable algorithms to tackle different machine learning problems
• Apply machine learning algorithms to datasets

Course Overview:

Introduction

• Generalization
• Underfitting, overfitting
• Cross-validation

Linear models

• Linear regression
• Classification with linear separators (mixtures of Gaussians, logistic regression, perceptron, support vector machines)

Non-linear models

• Non-linear basis functions
• Kernel methods
• Deep neural networks

Unsupervised learning

• Clustering

Sequence learning

• Hidden Markov models
• Recurrent and recursive neural networks

Ensemble learning

• Bagging
• Boosting

Large scale learning

• Distributed learning
• Stream learning

End user issues of Machine Learning

Real-world applications of Machine Learning

Topics in Machine Learning