This introductory machine learning course will give an overview of many models and algorithms used in modern machine learning including linear models, multi-layer neural networks, support vector machines, density estimation methods, Bayesian belief networks, clustering, ensemble methods, and reinforcement of learning. The course will give the student the basic ideas and intuition behind these methods as well as a more formal understanding of how and why they work. Through homework assignments students will have an opportunity to experiment with many machine learning techniques and apply them to various real-world datasets.

  • Credits: 3
  • Frequency: At least once a year


Requirements and Grading

Homework, team project, exams.