Introduction to Machine Learning

10-301 + 10-601, Spring 2019
School of Computer Science
Carnegie Mellon University


Important Notes

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

Lecture Videos

  • Panopto folder: [Andrew ID Required] 10-601 B/D
  • The above folders contains all the lecture video recordings for the specified section(s). To access them, please select Sign in using "Canvas".
  • We have also included links to individual videos below -- however, we recommend checking the link above to find the latest videos.

Tentative Schedule

Date Lecture Readings Announcements

Classification

Mon, 14-Jan Lecture 1 : Course Overview
[Slides] [Video]

Wed, 16-Jan Lecture 2 : Decision Trees
[Slides] [Video]

HW1 out

Fri, 18-Jan Recitation: HW1
[Video]

Mon, 21-Jan (No Class: Martin Luther King Day)

Wed, 23-Jan Lecture 3 : Decision Trees
[Slides] [Video] [Poll]

HW2 out

HW1 due

Fri, 25-Jan Recitation: HW2
[Video]

Mon, 28-Jan Lecture 4 : k-Nearest Neighbors
[Slides] [Video] [Poll]

Wed, 30-Jan Lecture 5 : Model Selection
[Slides] [Video] [Poll]

Fri, 1-Feb (No Recitation)

Mon, 4-Feb Lecture 6 : Perceptron
[Slides] [Video]

Linear Models

Wed, 6-Feb Lecture 7 : Linear Regression
[Slides] [Video]
  • Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.

HW3 out

HW2 due

Fri, 8-Feb Recitation: HW3
[Video]

Mon, 11-Feb Lecture 8 : Optimization for ML
[Slides] [Video]

Wed, 13-Feb Lecture 9 : Logistic Regression
[Slides] [Video]
  • Logistic Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 1.4.6, 8.1-8.3, 8.6.

Fri, 15-Feb (No recitation)

HW4 out

HW3 due

Mon, 18-Feb Lecture 10 : Regularization / Midterm Exam Review
[Slides] [Video]

Deep Learning

Wed, 20-Feb Lecture 11 : Neural Networks
[Video]

Thu, 21-Feb Midterm Exam 1 (Evening Exam) -- details will be announced on Piazza

Fri, 22-Feb Recitation: HW4
[Video]

Mon, 25-Feb Lecture 12 : Backpropagation
[Video]

Wed, 27-Feb Lecture 13 : Backpropagation (cont.)
[Video]
  • [Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

Fri, 1-Mar Recitation: HW5
[Video]

HW5 out

HW4 due

Learning Theory

Mon, 4-Mar Lecture 14 : Learning Theory: PAC Learning
[Video]

Wed, 6-Mar Lecture 15 : Learning Theory: Structured Risk Minimization
[Video]

Fri, 8-Mar (No class: Mid-semester break)

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

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

Fri, 15-Mar (No class: Spring break)

Generative Models

Mon, 18-Mar Lecture 16 : MLE and MAP
[Video]

Wed, 20-Mar Lecture 17 : Naive Bayes
[Video]

Fri, 22-Mar Recitation: HW6

HW6 out

HW5 due

Graphical Models

Mon, 25-Mar Lecture 18 : Hidden Markov Models (Part I)
[Video]

Wed, 27-Mar Lecture 19 : Hidden Markov Models (Part II)
[Video]

Fri, 29-Mar Recitation: HW7

HW7 out

HW6 due

Mon, 1-Apr Lecture 20 : Bayesian Networks
[Video]

Learning Paradigms

Wed, 3-Apr Lecture 21 : Reinforcement Learning: Value/Policy Iteration
[Video]

Thu, 4-Apr Midterm Exam 2 (Evening Exam) -- details will be announced on Piazza

Fri, 5-Apr (No recitation)

Mon, 8-Apr Lecture 22 : Reinforcement Learning: Q-Learning
[Video]

Wed, 10-Apr Lecture 23 : Deep Reinforcement Learning
[Video]

HW8 out

HW7 due

Fri, 12-Apr (No class: Spring Carnival)

Mon, 15-Apr Lecture 24 : SVMs
[Video]

Wed, 17-Apr Lecture 25 : Kernels / K-Means
[Video]

Fri, 19-Apr Recitation: HW8

Mon, 22-Apr Lecture 26 : PCA / Boosting
[Video]

Wed, 24-Apr Lecture 27 : Expectation Maximization
[Video]

HW9 out

HW8 due

Fri, 26-Apr Recitation: HW9

Mon, 29-Apr Lecture 28 : TBD
[Video]

Wed, 1-May Lecture 29 : Final Exam Review
[Video]

Fri, 3-May Recitation: Final Exam Review

HW9 due

May 6 - 13 Final Exam Period