Introduction to Machine Learning

10-301 + 10-601, Spring 2020
School of Computer Science
Carnegie Mellon University

Important Notes

This schedule is tentative and subject to change. Please check back often.

Lecture Videos

Tentative Schedule

Date Lecture Readings Announcements


Mon, 13-Jan Lecture 1 : Course Overview
[Slides] [Whiteboard] [Video]

Wed, 15-Jan Lecture 2 : Decision Trees
[Slides] [Whiteboard] [Video]

HW1 out

Fri, 17-Jan Recitation: HW1

Mon, 20-Jan (No Class: Martin Luther King Day)

Wed, 22-Jan Lecture 3 : Decision Trees
[Slides] [Whiteboard] [Video] [Poll]

HW1 due

Thu, 23-Jan

HW2 out

Fri, 24-Jan Recitation: HW2

Mon, 27-Jan Lecture 4 : k-Nearest Neighbors
[Slides] [Whiteboard] [Video] [Poll]

Wed, 29-Jan Lecture 5 : Model Selection
[Whiteboard] [Video] [Poll]

Fri, 31-Jan Recitation: Debugging

Mon, 3-Feb Lecture 6 : Perceptron
[Whiteboard] [Video] [Poll]

Linear Models

Wed, 5-Feb Lecture 7 : Linear Regression
[Whiteboard] [Video] [Poll]
  • Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.

HW2 due

Thu, 6-Feb

HW3 out

Fri, 7-Feb Recitation: HW3

Mon, 10-Feb Lecture 8 : Optimization for ML
[Whiteboard] [Video] [Poll]

Wed, 12-Feb Lecture 9 : Midterm Exam Review / Logistic Regression
[Whiteboard] [Video] [Poll]
  • Logistic Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 1.4.6, 8.1-8.3, 8.6.

HW3 due

Fri, 14-Feb (No recitation)

Mon, 17-Feb Lecture 10 : Multinomial Logistic Regression / Feature Engineering

Tue, 18-Feb Midterm Exam 1 (Evening Exam) -- details will be announced on Piazza

Deep Learning

Wed, 19-Feb Lecture 11 : Regularization / Neural Networks

HW4 out

Fri, 21-Feb Recitation: HW4

Mon, 24-Feb Lecture 12 : Neural Networks

Wed, 26-Feb Lecture 13 : Backpropagation

Fri, 28-Feb Recitation: HW5

HW4 due

Sat, 29-Feb

HW5 out

Mon, 2-Mar Lecture 14 : Deep Learning
  • [Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

Learning Theory

Wed, 4-Mar Lecture 15 : Learning Theory: PAC Learning

Fri, 6-Mar (No class: Mid-semester break)

Mon, 9-Mar (No class: Spring break)

Wed, 11-Mar (No class: Spring break)

Fri, 13-Mar (No class: Spring break)

Mon, 16-Mar Lecture 16 : Learning Theory: PAC Learning

Generative Models

Wed, 18-Mar Lecture 17 : MLE/MAP

HW5 due

Thu, 19-Mar

HW6 out

Fri, 20-Mar Recitation: HW6

Graphical Models

Mon, 23-Mar Lecture 18 : Naive Bayes

Wed, 25-Mar Lecture 19 : Midterm Exam Review / Hidden Markov Models (Part I)

HW6 due

Fri, 27-Mar (No recitation)

Mon, 30-Mar Lecture 20 : Hidden Markov Models (Part II)

Tue, 31-Mar Midterm Exam 2 (Evening Exam) -- details will be announced on Piazza

Wed, 1-Apr Lecture 21 : Bayesian Networks

HW7 out

Fri, 3-Apr Recitation: HW7

Learning Paradigms

Mon, 6-Apr Lecture 22 : Reinforcement Learning: Markov Decision Processes

Wed, 8-Apr Lecture 23 : Reinforcement Learning: Value/Policy Iteration

Fri, 10-Apr Recitation: HW8

HW7 due

Sat, 11-Apr

HW8 out

Mon, 13-Apr Lecture 24 : Reinforcement Learning: Q-Learning

Wed, 15-Apr Lecture 25 : Deep Reinforcement Learning / K-Means

Fri, 17-Apr

Mon, 20-Apr Lecture 26 : Dimensionality Reduction: PCA

Wed, 22-Apr Lecture 27 : SVMs / Kernel Methods

HW8 due

Thu, 23-Apr

HW9 out

Fri, 24-Apr Recitation: HW9

Mon, 27-Apr Lecture 28 : Ensemble Methods / Recommender Systems

Wed, 29-Apr Lecture 29 : Final Exam Review

HW9 due

Fri, 1-May (No recitation)

May 4 - 12 Final Exam Period -- exact time/date of final exam is scheduled by the registrar