MYZ 309E - Artificial Intelligence in Mathematical Engineering

Course Objectives

This course aims to:

I. Provide a foundational understanding of commonly used statistical learning methods for inference and prediction in data analysis,
II. Cover both supervised learning (regression and classification) and unsupervised learning (clustering) techniques, and
III. Facilitate the practical application of these learning methods to real-world data analysis problems using Python.
IV. Equip students with essential data analysis skills while enhancing their critical thinking and problem-solving abilities, and develop their analytical report writing skills.

Course Description

Best practices in data analysis. Statistics, data science, machine learning, and artificial intelligence: similarities and differences. Differences between supervised and unsupervised learning algorithms. Linear regression, regularization methods (Lasso and Ridge), classification techniques (logistic regression, support vector machines, tree-based methods), model evaluation strategies (cross-validation and performance metrics). Dimensionality reduction and principal component analysis. Clustering algorithms. Artificial neural networks. Applications of artificial intelligence.