erwinhorvath24/cap5625-computational-foundations-of-artifical-intelligence

CAP5625-Computational-Foundations-of-Artifical-Intelligence

Course Description

This course covers the mathematical and programming foundations of artificial intelligence (AI) and machine learning (ML) using contemporary programming languages and tools. As a result, students develop familiarity with mathematical methods (and associated notation, software packages and libraries) that are widely used in AI and ML projects and literature.

Course Objectives/Student Learning Outcomes

By the end of the course, students will be able to:

1. Understand the mathematical foundations of machine learning.
2. Demonstrate proficiency in solving machine learning problems.
3. Identify and apply statistical and computational models to machine learning problems.
4. Analyze the performance of particular machine learning models, and justify their use and limitations.

Course Topical Outline

Topic 1: Introduction

Topic 2: Introduction to regression

Topic 3: Linear and non-linear regression and model selection

Topic 4: Feature selection and regularization

Topic 5: Advanced regularization techniques

Topic 6: Discriminant analysis

Topic 7: Logistic regression

Topic 8: Support vector machines

Topic 9: Neural networks

Topic 10: Random forests and boosting

Topic 11: Unsupervised learning