Course Overview

Course Description:

Machine learning is a field devoted to developing algorithms that adapt their behaviour to data, providing useful representations of the data and/or predictions. This course covers some fundamental theoretical concepts in machine learning, and common patterns for implementing methods in practice. The intended audience are those wanting the background required to begin research and development of machine learning methods.

Lectures: Prof. Dimitris Korobilis

Labs: Miguel C. Herculano

Class Schedule:

Lectures: Every Thursday between 10:00-12:00, from the 25th of September and
Labs: Every Thursday between 14:00-16:00 from the 25th of September.

PRELIMINARY

• Getting Started
  • About Python
  • Setting up Your Python Environment
  • An Introductory Example
  • Learn More
  • Exercises

LAB MATERIALS

• Introduction to Regression
• Linear Regression
• Multivariate Linear Regression
• Nonlinear Relationships in Linear Regression
• Maximum Likelihood Estimation
• Conditional Distributions
• Maximum Likelihood Estimation
• Penalized Regression
• Bayesian Linear Regression
• Introduction to Classification
• Logistic Regression
• Decision Trees, Random Forests & Support Vector Machines
• Ensemble Learning
• Gaussian Process Regression