Introduction to Machine Learning

Important Notes

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

Tentative Schedule

Date	Lecture	Readings	Announcements
Classification & Regression
Mon, 25-Aug	Lecture 1 : Course Overview [Slides] [Slides (Inked)]	Command Line and File I/O Tutorial. 10601 Course Staff (2020). 10601 Learning Objectives. Matt Gormley (2023). Math Resources. 10601 Course Staff (2023).	HW1 Out
Wed, 27-Aug	Lecture 2 : Machine Learning as Function Approximation [Slides] [Slides (Inked)]	10601 Notation Crib Sheet. Matt Gormley (2023).
Fri, 29-Aug	Recitation: HW1 [Handout] [Solutions]
Mon, 1-Sep	(Labor Day - No Class)
Wed, 3-Sep	Lecture 3 : Decision Trees [Slides] [Slides (Inked)] [Poll]	Visual Information Theory. Christopher Olah (2015). blog. Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.	HW1 Due HW2 Out
Fri, 5-Sep	Recitation: HW2 [Handout] [Solutions]
Mon, 8-Sep	Lecture 4 : k-Nearest Neighbors [Slides] [Slides (Inked)] [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 10-Sep	Lecture 5 : Model Selection and Experimental Design [Slides] [Slides (Inked)] [Poll]	Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.
Linear Models
Fri, 12-Sep	Lecture 6 : Perceptron [Slides] [Slides (Inked)] [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4. Perceptron Demo. Geoff Gordon (2025).
Mon, 15-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, 17-Sep	Lecture 8 : Optimization for ML [Slides] [Slides (Inked)] [Poll]	An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).
Fri, 19-Sep	Quiz 1 + Recitation: HW3 [Handout] [Solutions]		Exam 1 Practice Problems out
Mon, 22-Sep	Lecture 9 : Stochastic Gradient Descent / Logistic Regression [Slides] [Slides (Inked)] [Poll]	Estimating Probabilities: MLE and MAP. Tom Mitchell (2016). Probability and Information Theory. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 3. Logistic Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 1.4.6, 8.1-8.3, 8.6.	HW3 Due (only two grace/late days permitted)
Wed, 24-Sep	Lecture 10 : Feature Engineering / Regularization [Slides] [Slides (Inked)] [Poll]	Regularization for Deep Learning. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 7.1 and 7.8.
Fri, 26-Sep	Exam 1 Review OH
Neural Networks
Mon, 29-Sep	Lecture 11 : Neural Networks [Slides] [Slides (Inked)] [Poll]	Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.
Mon, 29-Sep	Exam 1 (evening exam, details will be announced on Piazza)		HW4 Out
Wed, 1-Oct	Lecture 12 : Backpropagation I [Slides] [Slides (Inked)] [Poll]	Deep Feedforward Networks. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 6.5. Matrix Calculus for 10-301/601. Hoeseong (Hayden) Kim, Abhishek Vijayakumar (2022). 10601 Course Staff.
Fri, 3-Oct	Recitation: HW4 [Handout] [Solutions]
Mon, 6-Oct	Lecture 13 : Backpropagation II [Slides] [Slides (Inked)] [Poll]	Numpy Notes. 10601 Course Staff (2021). Logging Tutorial. 10601 Course Staff (2021). Workflow and Debugging Slides. 10601 Course Staff (2021).
Societal Impacts
Wed, 8-Oct	Lecture 14 : Societal Impacts of ML [Slides] [Slides (Inked)] [Poll]		HW4 Due HW5 Out
Fri, 10-Oct	Recitation: HW5 [Handout] [Solutions]
Mon, 13-Oct	Fall break
Tue, 14-Oct
Wed, 15-Oct	Fall break
Thu, 16-Oct
Fri, 17-Oct	Fall break
Learning Theory
Mon, 20-Oct	Lecture 15 : PAC learning [Slides] [Slides (Inked)] [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Wed, 22-Oct	Lecture 16 : PAC Learning / MLE & MAP [Slides] [Slides (Inked)] [Poll]	A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).
Fri, 24-Oct	Recitation: HW6
Sun, 26-Oct			HW5 Due HW6 Out, Exam 2 Practice Problems out
Deep Learning
Mon, 27-Oct	Lecture 17 : MLE & MAP / CNNs and RNNs [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Wed, 29-Oct	Lecture 18 : RNN-LMs and Transformers-LMs [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Fri, 31-Oct	Quiz 2 + Exam 2 Review OH
Sat, 1-Nov			HW6 Due (only two grace/late days permitted)
Mon, 3-Nov	Lecture 19 : AutoDiff, Pre-training, Fine-Tuning, In-context Learning [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Reinforcement Learning
Wed, 5-Nov	Lecture 20 : Reinforcement Learning: MDPs [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Thu, 6-Nov	Exam 2 (evening exam, details will be announced on Piazza)		HW7 Out
Fri, 7-Nov	Recitation: HW7
Mon, 10-Nov	Lecture 21 : Reinforcement Learning: Value/Policy Iteration [Poll]
Wed, 12-Nov	Lecture 22 : Reinforcement Learning: Q-Learning / Deep RL [Poll]
Fri, 14-Nov	Recitation: HW8
Sun, 16-Nov			HW7 Due HW8 Out
Mon, 17-Nov	Lecture 23 : Recommender Systems / Ensemble Methods: Boosting [Poll]
Learning Paradigms
Wed, 19-Nov	Lecture 24 : Ensemble Methods: Bagging -- swap with recsys? [Poll]
Fri, 21-Nov	Recitation: HW9
Mon, 24-Nov	Lecture 25 : K-Means / Dimensionality Reduction: PCA [Poll]		HW8 Due HW9 Out
Wed, 26-Nov	Thanksgiving Break - No Class
Thu, 27-Nov	Thanksgiving Break - No Class	k-means++: The Advantages of Careful Seeding. Arthur & Vassilvitskii (2006). A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).
Fri, 28-Nov	Thanksgiving Break - No Class
Mon, 1-Dec	Lecture 26 : Special Topics: Generative Models for Vision / Significance Testing for ML [Poll]
Wed, 3-Dec	Lecture 27 : Special Topics: Bayesian Methods for Machine Learning [Poll]
Fri, 5-Dec	Quiz 3 + Exam 3 Review OH
Dec-8 to Dec-15	Exam 3 (during Final Exam Period -- exact time/date TBD by the registrar, details will be announced on Piazza)

Important Notes

Tentative Schedule

Classification & Regression

Linear Models

Neural Networks

Societal Impacts

Learning Theory

Deep Learning

Reinforcement Learning

Learning Paradigms