CS480/680 Winter 2023 - 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

Classic models

• K nearest neighbours
• Decision trees
• Linear regression
• Logistic regression

Kernel methods

• Support vector machines
• Gaussian processes

Deep Learning

• Fully connected neural networks
• Convolutional neural networks
• (Variational) autoencoders
• Generative adversarial networks
• Recurrent neural networks
• Attention and transformers
• Normalizing flows
• Diffusion models

Ensemble learning

• Bagging
• Boosting
• Decision forests

End user issues of Machine Learning

Real-world applications of Machine Learning