Introduction to Machine Learning

About the course

The course builds machine learning from the ground up: fitting a first model by hand, estimating its performance honestly, and moving through classification, trees and ensembles to neural networks, deep learning and the methods that work without labels. It closes with two questions every model must answer: how uncertain its predictions are, and for whom it fails.

Each chapter is organized around a single question. The lectures run live code in the browser, so each idea can be tried as soon as it is introduced.

Offered as: DS 3000, undergraduate
Program: Artificial Intelligence Systems Engineering
Instructor: Fadi AlMahamid, Ph.D.
Content: 10 chapters, 25 topics
Lecture slides: Enrolled students, with a username and password

Course content

The ten chapters progress from a straight line fitted to five points to classification and an honest estimate of how well a model performs.

Chapter 1

Introduction to Machine Learning

What is a model, and what is it actually searching for?

In this chapter
1. The landscape
2. The three learning paradigms
3. From data to a result you trust
4. Linear regression, your first model
5. Finding the best line
Slides for chapter 1
Chapter 2

Multiple Linear Regression

What changes when a prediction depends on more than one thing?

In this chapter
1. From one feature to many
2. Solving for the weights
3. Choosing a loss
4. Judging generalization
5. Exploring a real dataset
Slides for chapter 2
Chapter 3

Probability and Likelihood

Which parameters make the observed data most probable?

In this chapter
1. Why probability
2. Random variables
3. Maximum likelihood estimation
4. Least squares as MLE
5. In code
Slides for chapter 3
Chapter 4

Classification

How is a label predicted, and how good is the prediction?

In this chapter
1. Classification and linear classifiers
2. Logistic regression
3. SVM and k-nearest neighbors
4. Loss functions and evaluation
5. In code
Slides for chapter 4
Chapter 5

Estimating Performance

How do you measure a model honestly and choose between models?

In this chapter
1. Bias and variance
2. Choosing a model
3. Constructing and selecting features
4. Regularization
5. In code
Slides for chapter 5
Chapter 6

Trees and Forests

How do yes-or-no questions become a model, and why do many trees beat one?
Slides for chapter 6
Chapter 7

Neural Networks

How do layers of simple units learn a function no one wrote down?
Slides for chapter 7
Chapter 8

Clustering and Dimensionality Reduction

What structure is in data that comes with no answers?
Slides for chapter 8
Chapter 9

Deep Learning and Generative AI

How do deep networks read images and generate new data?
Slides for chapter 9
Chapter 10

Uncertainty, Fairness and Causality

How sure is a prediction, is it fair, and does it show a cause?
Slides for chapter 10

Lecture slides

The lectures are interactive decks with animated figures that you can play and step through, and Python cells that run in the browser. They are available to enrolled students, who sign in with the username and password provided by the instructor.

Reading

Primary material

The lecture slides

There is no required textbook. The lecture slides are the primary material, and each chapter names its own sources.

Introduction to Machine Learning

About the course

Course content