Introduction to Machine Learning

10-301 + 10-601, Fall 2024
School of Computer Science
Carnegie Mellon University


Important Notes

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

Tentative Schedule

Date Lecture Readings Announcements

Classification & Regression

Mon, 26-Aug Lecture 1 : Course Overview
[Slides] [Slides (Inked)]

HW1 Out

Wed, 28-Aug Lecture 2 : Machine Learning as Function Approximation
[Slides] [Slides (Inked)]

Fri, 30-Aug Recitation: HW1
[Handout] [Solutions]

Mon, 2-Sep (Labor Day - No Class)

Wed, 4-Sep Lecture 3 : Decision Trees
[Slides] [Slides (Inked)] [Poll]

HW1 Due

HW2 Out

Fri, 6-Sep Recitation: HW2
[Handout] [Solutions]

Mon, 9-Sep Lecture 4 : k-Nearest Neighbors
[Slides] [Slides (Inked)] [Poll]

Wed, 11-Sep Lecture 5 : Model Selection and Experimental Design
[Slides] [Slides (Inked)] [Poll]

Linear Models

Fri, 13-Sep Lecture 6 : Perceptron
[Slides] [Slides (Inked)] [Poll]

Mon, 16-Sep Lecture 7 : Linear Regression
[Slides] [Slides (Inked)] [Poll]
  • Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.

HW2 Due

HW3 Out

Wed, 18-Sep Lecture 8 : Optimization for ML
[Slides] [Slides (Inked)] [Poll]

Fri, 20-Sep Recitation: HW3
[Handout] [Solutions]

Exam 1 Practice Problems out

Mon, 23-Sep Lecture 9 : Stochastic Gradient Descent / Logistic Regression
[Slides] [Slides (Inked)] [Poll]

HW3 Due (only two grace/late days permitted)

Wed, 25-Sep Lecture 10 : Feature Engineering / Regularization
[Slides] [Slides (Inked)] [Poll]

Fri, 27-Sep Exam 1 Review OH

Neural Networks

Mon, 30-Sep Lecture 11 : Neural Networks
[Slides] [Slides (Inked)] [Poll]

Mon, 30-Sep Exam 1 (evening exam, details will be announced on Piazza) 6:30p - 8:30p

HW4 Out

Wed, 2-Oct Lecture 12 : Backpropagation I
[Slides] [Slides (Inked)] [Poll]

Fri, 4-Oct Recitation: HW4
[Handout] [Solutions]

Mon, 7-Oct Lecture 13 : Backpropagation II
[Slides] [Slides (Inked)] [Poll]

Societal Impacts

Wed, 9-Oct Lecture 14 : Societal Impacts of ML
[Slides] [Slides (Inked)] [Poll]

HW4 Due

HW5 Out

Fri, 11-Oct Recitation: HW5
[Handout] [Solutions]

Mon, 14-Oct (Fall Break - No Class)

Tue, 15-Oct

Wed, 16-Oct (Fall Break - No Class)

Thu, 17-Oct

Fri, 18-Oct (Fall Break - No Class)

Learning Theory

Mon, 21-Oct Lecture 15 : PAC Learning
[Slides] [Poll]

Wed, 23-Oct Lecture 16 : PAC Learning / MLE & MAP
[Poll]

Fri, 25-Oct Recitation: HW6

Sun, 27-Oct
  • Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

HW5 Due

HW6 Out, Exam 2 Practice Problems out

Deep Learning

Mon, 28-Oct Lecture 17 : CNNs and RNNs
[Poll]

Wed, 30-Oct Lecture 18 : RNN-LMs and Transformers-LMs
[Poll]

Fri, 1-Nov Exam 2 Review OH

Sat, 2-Nov

HW6 Due (only two grace/late days permitted)

Mon, 4-Nov Lecture 19 : AutoDiff, Pre-training, Fine-Tuning, In-context Learning
[Poll]

Reinforcement Learning

Wed, 6-Nov Lecture 20 : Reinforcement Learning: MDPs
[Poll]

Thu, 7-Nov Exam 2 (evening exam, details will be announced on Piazza) 6:30p - 8:30p

HW7 Out

Fri, 8-Nov Recitation: HW7

Mon, 11-Nov Lecture 21 : Reinforcement Learning: Value/Policy Iteration
[Poll]

Wed, 13-Nov Lecture 22 : Reinforcement Learning: Q-Learning / Deep RL
[Poll]

Fri, 15-Nov Recitation: HW8

Sun, 17-Nov

HW7 Due

HW8 Out

Learning Paradigms

Mon, 18-Nov Lecture 23 : Dimensionality Reduction: PCA
[Poll]

Wed, 20-Nov Lecture 24 : K-Means / Ensemble Methods: Bagging
[Poll]

Fri, 22-Nov No Recitation

Mon, 25-Nov Lecture 25 : Ensemble Methods: Boosting / Recommender Systems
[Poll]

HW8 Due

HW9 Out

Wed, 27-Nov (Thanksgiving Break - No Class)

Thu, 28-Nov

Fri, 29-Nov (Thanksgiving Break - No Class)

Mon, 2-Dec Recitation: HW9

Exam 3 Practice Problems out

Wed, 4-Dec Lecture 26 : Special Topics: Generative Models for Vision / Significance Testing for ML
[Poll]

Thu, 5-Dec

HW9 Due (only two grace/late days permitted)

Fri, 6-Dec Exam 3 Review OH