10-301 + 10-601, Fall 2023

Important Notes

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

You can access the OneNote notebook containing all whiteboards from lecture/recitation here. The PDF version of each whiteboard is linked below.

Tentative Schedule

Date	Lecture	Readings	Announcements
Classification & Regression
Mon, 28-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).
Wed, 30-Aug	Lecture 2 : Machine Learning as Function Approximation [Slides] [Slides (Inked)]	10601 Notation Crib Sheet. Matt Gormley (2023).
Fri, 1-Sep	Background Test (in-class, required)		HW1 Out
Sat, 2-Sep	Recitation: HW1 (video recording only) [Handout] [Solutions] [Video]
Mon, 4-Sep	Labor Day
Wed, 6-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, 8-Sep	Recitation: HW2 [Handout] [Solutions]
Mon, 11-Sep	Lecture 4 : k-Nearest Neighbors [Slides] [Slides (Inked)] [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 13-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, 15-Sep	Lecture 6 : Perceptron [Slides] [Slides (Inked)] [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.	HW2 Due HW3 Out
Sat, 16-Sep	Short Video: Decision Trees with Real-Valued Features
Mon, 18-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.	Exam 1 Practice Problems out
Wed, 20-Sep	Recitation: HW3 [Handout] [Solutions]
Fri, 22-Sep	Exam 1 Review [Solutions] [Supplemental Solutions]
Sat, 23-Sep			HW3 due (only two grace/late days permitted)
Mon, 25-Sep	Lecture 8 : Optimization for ML [Slides] [Slides (Inked)] [Poll]	An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).
Wed, 27-Sep	Lecture 9 : Stochastic Gradient Descent / Logistic Regression [Slides] [Slides (Inked)] [Poll]	[Required] Estimating Probabilities: MLE and MAP. Tom Mitchell (2016). [Optional] 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.
Thu, 28-Sep	Exam 1 (evening exam, details will be announced on Piazza)
Fri, 29-Sep	Recitation: HW4 [Handout] [Solutions]		HW4 Out
Mon, 2-Oct	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.
Neural Networks
Wed, 4-Oct	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.
Fri, 6-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.
Mon, 9-Oct	Lecture 13 : Backpropagation II [Slides] [Slides (Inked)] [Poll]		HW4 Due HW5 Out
Wed, 11-Oct	Recitation: HW5 [Handout] [Solutions]	Numpy Notes. 10601 Course Staff (2021). Logging Tutorial. 10601 Course Staff (2021). Workflow and Debugging Slides. 10601 Course Staff (2021).
Learning Theory
Fri, 13-Oct	Lecture 14 : PAC Learning [Slides] [Slides (Inked)] [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Mon, 16-Oct	(Fall Break - No Class)
Tue, 17-Oct
Wed, 18-Oct	(Fall Break - No Class)
Thu, 19-Oct
Fri, 20-Oct	(Fall Break - No Class)
Mon, 23-Oct	Lecture 15 : PAC Learning / MLE+MAP [Slides] [Slides (Inked)] [Poll]	[Required] Estimating Probabilities: MLE and MAP. Tom Mitchell (2016). [Optional] Probability and Information Theory. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 3. A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).
Wed, 25-Oct	Lecture 16 : Naive Bayes [Slides] [Slides (Inked)] [Poll]	Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Tom Mitchell (2016, draft).
Fri, 27-Oct	Recitation: HW6 [Handout] [Solutions]		HW5 Due HW6 Out
Deep Learning
Mon, 30-Oct	Lecture 17 : Foundations: RNNs and CNNs [Slides] [Slides (Inked)] [Poll]	[Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.	Exit Poll: Exam 1 due Exam 2 Practice Problems out
Wed, 1-Nov	Lecture 18 : Transformers and Autodiff [Slides] [Slides (Inked)] [Poll]
Fri, 3-Nov	Exam 2 Review		HW6 Due (only two grace/late days permitted)
Mon, 6-Nov	Lecture 19 : Pre-training, Fine-tuning, In-context Learning [Slides] [Slides (Inked)] [Poll]
Reinforcement Learning
Wed, 8-Nov	Lecture 20 : Reinforcement Learning: MDPs [Slides] [Slides (Inked)] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Thu, 9-Nov	Exam 2 (evening exam, details will be announced on Piazza)
Fri, 10-Nov	Recitation: HW7 [Handout] [Solutions] [Supplemental Material]		HW7 Out
Mon, 13-Nov	Lecture 21 : Reinforcement Learning: Value/Policy Iteration [Slides] [Slides (Inked)] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 15-Nov	Lecture 22 : Reinforcement Learning: Q-Learning / Deep RL [Slides] [Slides (Inked)] [Poll]	[Optional] Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).
Fri, 17-Nov	Recitation: HW8 [Handout] [Solutions]
Learning Paradigms
Mon, 20-Nov	Lecture 23 : Dimensionality Reduction: PCA [Slides] [Slides (Inked)] [Poll]	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).	HW7 Due HW8 Out
Wed, 22-Nov	(Thanksgiving - No class)
Thu, 23-Nov	(Thanksgiving - No class)
Fri, 24-Nov	(Thanksgiving - No class)
Mon, 27-Nov	Lecture 24 : K-Means / Ensemble Methods: Bagging [Slides] [Slides (Inked)] [Poll]	k-means++: The Advantages of Careful Seeding. Arthur & Vassilvitskii (2006). Ensemble Methods. Hal Daumé III (2017). CIML, Chapter 13.
Wed, 29-Nov	Lecture 25 : Ensemble Methods: Boosting / Recommender Systems [Slides] [Slides (Inked)] [Poll]	A Brief Introduction to Boosting. Robert E. Schapire (1999). Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).
Fri, 1-Dec	Recitation: HW9 [Handout] [Solutions]		HW8 Due HW9 Out
Mon, 4-Dec	Lecture 26 : Special Topics: Societal Impacts of ML [Slides] [Slides (Inked)] [Poll]
Wed, 6-Dec	Lecture 27 : Special Topics: Generative Models for Vision / Significance Testing for ML [Slides] [Slides (Inked)] [Poll]
Thu, 7-Dec			HW9 due (only two grace/late days permitted) Exam 3 Practice Problems out
Fri, 8-Dec	Exam 3 Review	10601 Learning Objectives. Matt Gormley (2023).
Tue, 12-Dec	Exam 3 (5:30pm - 8:30pm, details will be announced on Piazza)

Introduction to Machine Learning

Important Notes

Tentative Schedule

Classification & Regression

Linear Models

Neural Networks

Learning Theory

Deep Learning

Reinforcement Learning

Learning Paradigms