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

Classification

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
[Video]

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
[Video]

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
[Video]

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
[Video]

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
[Video]

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

Deep Learning

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

HW4 out

Fri, 21-Feb Recitation: HW4
[Video]

Mon, 24-Feb Lecture 12 : Neural Networks
[Video]

Wed, 26-Feb Lecture 13 : Backpropagation
[Video]

Fri, 28-Feb Recitation: HW5
[Video]

HW4 due

Sat, 29-Feb

HW5 out

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

Learning Theory

Wed, 4-Mar Lecture 15 : Learning Theory: PAC Learning
[Video]

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
[Video]

Generative Models

Wed, 18-Mar Lecture 17 : MLE/MAP
[Video]

HW5 due

Thu, 19-Mar

HW6 out

Fri, 20-Mar Recitation: HW6
[Video]

Graphical Models

Mon, 23-Mar Lecture 18 : Naive Bayes
[Video]

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

HW6 due

Fri, 27-Mar (No recitation)

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

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

Wed, 1-Apr Lecture 21 : Bayesian Networks
[Video]

HW7 out

Fri, 3-Apr Recitation: HW7
[Video]

Learning Paradigms

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

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

Fri, 10-Apr Recitation: HW8
[Video]

HW7 due

Sat, 11-Apr

HW8 out

Mon, 13-Apr Lecture 24 : Reinforcement Learning: Q-Learning
[Video]

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

Fri, 17-Apr

Mon, 20-Apr Lecture 26 : Dimensionality Reduction: PCA
[Video]

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

HW8 due

Thu, 23-Apr

HW9 out

Fri, 24-Apr Recitation: HW9
[Video]

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

Wed, 29-Apr Lecture 29 : Final Exam Review
[Video]

HW9 due

Fri, 1-May (No recitation)

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