Over Summer 2022 at Northeastern university, I was a part-time lecturer for the Electrical & Computer Engineering department’s introductory course on machine learning. Here I briefly describe the course content and then provide links to my lectures. The course also has a complementary Git codebase.

Course Overview

Machine learning is the study and design of algorithms that enable computers/machines to learn from experience/data. The course I taught was an introductory course on machine learning covering a broad range of fundamental algorithms, focusing on the underlying models behind each approach, enabling students to learn where and how to apply learning algorithms, as well as why they work. The course placed emphasis on equipping students with the practical and theoretical foundations to prepare them for a future career in machine learning.

Subjects covered: Bayesian decision theory, maximum likelihood parameter estimation, logistic regression, linear regression, dimensionality reduction, model selection, support vector machines, neural networks and unsupervised clustering.

Lecture Slides

Below are a subset of the lectures I delivered for the course, specifically those for which I created the slides and material myself.

• Course Overview & Basics

• Foundations:
  • Linear Algebra
  • Probability & Statistics
  • Numerical Optimization (Gradient Descent)

• Introductory Algorithms:
  • Linear Discriminant Analysis (LDA)
  • Principal Component Analysis (PCA)

• Bayesian Decision Theory (Empirical Risk Minimization)

• Naïve Bayes

• Bayesian Parameter Estimation

• Logistic Regression

• Model Selection

• Regularization

• Neural Networks

• Unsupervised Clustering