10-301 + 10-601, Spring 2022

Important Notes

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

Tentative Schedule

Date	Lecture	Readings	Announcements
Classification & Regression
Wed, 19-Jan	Lecture 1 : Course Overview [Slides] [Whiteboard]	Command Line and File I/O Tutorial. 10601 Course Staff (2020). 10601 Learning Objectives. Matt Gormley (2021). Math Resources. 10601 Course Staff (2021).	HW1 Out
Fri, 21-Jan	Recitation: HW1 [Handout] [Solutions] [Whiteboard]
Mon, 24-Jan	Lecture 2 : Machine Learning as Function Approximation [Slides] [Whiteboard]	10601 Notation Crib Sheet. Matt Gormley (2018).
Wed, 26-Jan	Lecture 3 : Decision Trees [Slides] [Whiteboard] [Poll]	Visual Information Theory. Christopher Olah (2015). blog. Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.	HW1 Due HW2 Out
Fri, 28-Jan	Recitation 2: HW2 [Handout] [Solutions] [Whiteboard]
Mon, 31-Jan	Lecture 4 : k-Nearest Neighbors [Slides] [Whiteboard] [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 2-Feb	Lecture 5 : Model Selection and Experimental Design [Slides] [Whiteboard] [Poll]	Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.
Thu, 3-Feb			HW1 Solution Session
Fri, 4-Feb	Lecture 6 : Perceptron [Slides] [Whiteboard] [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.	HW2 Due HW3 Out
Linear Models
Mon, 7-Feb	Lecture 7 : Linear Regression [Slides] [Whiteboard] [Poll]	Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.
Wed, 9-Feb	Recitation: HW3 [Handout] [Solutions] [Whiteboard]
Fri, 11-Feb	(No Recitation)		HW3 due (only two grace/late days permitted) Mock Exam 1 and Exam 1 practice problems out
Mon, 14-Feb	Lecture 8 : Exam 1 Review / Optimization for ML [Slides] [Whiteboard] [Poll]	An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).	HW2 Solution Session
Tue, 15-Feb			HW3 Solution Session
Wed, 16-Feb	Lecture 9 : Stochastic Gradient Descent / Logistic Regression [Slides] [Whiteboard] [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, 17-Feb	Exam 1 (evening exam, details will be announced on Piazza)
Fri, 18-Feb	Recitation: HW4 [Handout] [Solutions] [Whiteboard]		HW4 Out
Mon, 21-Feb	Lecture 10 : Feature Engineering / Regularization [Slides] [Whiteboard] [Poll]	Regularization for Deep Learning. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 7.1 and 7.8.
Deep Learning
Wed, 23-Feb	Lecture 11 : Neural Networks [Slides] [Whiteboard] [Poll]	Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.
Fri, 25-Feb	Lecture 12 : Backpropagation [Slides] [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.
Sun, 27-Feb			HW4 Due HW5 Out
Mon, 28-Feb	Lecture 13 : Deep Learning [Slides] [Whiteboard] [Poll]	[Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Wed, 2-Mar	Recitation: HW5 [Handout] [Solutions] [Whiteboard]	Numpy Notes. 10601 Course Staff (2021). Logging Tutorial. 10601 Course Staff (2021). Workflow and Debugging Slides. 10601 Course Staff (2021).
Fri, 4-Mar	Mid-semester break
Mon, 7-Mar	Spring break
Wed, 9-Mar	Spring break
Fri, 11-Mar	Spring break
Learning Theory
Mon, 14-Mar	Lecture 14 : Deep Learning / Learning Theory: PAC Learning [Slides] [Whiteboard] [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Tue, 15-Mar			HW4 Solution Session
Wed, 16-Mar	Lecture 15 : Learning Theory: PAC Learning [Slides] [Whiteboard] [Poll]	A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).
Generative Models
Fri, 18-Mar	Lecture 16 : MLE/MAP [Slides] [Whiteboard] [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.	HW5 Due HW6 Out
Mon, 21-Mar	Lecture 17 : Naive Bayes [Slides] [Whiteboard] [Poll]	Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Tom Mitchell (2016, draft).
Wed, 23-Mar	Recitation: HW6 [Handout] [Solutions]
Thu, 24-Mar
Graphical Models
Fri, 25-Mar	Lecture 18 : Exam 2 Review / Hidden Markov Models (Part I) [Slides] [Whiteboard] [Poll]	[Only pages 257 - 266] A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Rabiner (1989).	HW6 Due (only two grace/late days permitted) Mock Exam 2 & Exam 2 practice problems out
Mon, 28-Mar	Lecture 19 : Hidden Markov Models (Part II) [Slides] [Whiteboard] [Poll]		HW5 Solution Session
Tue, 29-Mar			HW6 Solution Session
Wed, 30-Mar	Lecture 20 : Bayesian Networks [Slides] [Whiteboard] [Poll]	Directed Graphical Models (Bayes nets). Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 10.
Thu, 31-Mar	Exam 2 (evening exam, details will be announced on Piazza)
Fri, 1-Apr	Recitation: HW7 [Handout] [Solutions]		HW7 Out
Reinforcement Learning
Mon, 4-Apr	Lecture 21 : Reinforcement Learning: MDPs [Slides] [Whiteboard] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 6-Apr	Lecture 22 : Reinforcement Learning: Value/Policy Iteration [Slides] [Whiteboard] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Fri, 8-Apr	(No Classes: Carnival)
Mon, 11-Apr	Lecture 23 : Reinforcement Learning: Q-Learning [Slides] [Whiteboard] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Tue, 12-Apr			HW7 Due HW8 Out
Wed, 13-Apr	Lecture 24 : Deep Reinforcement Learning [Slides] [Whiteboard] [Poll]	[Optional] Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).
Fri, 15-Apr	Recitation: HW8 [Handout] [Solutions]
Learning Paradigms
Mon, 18-Apr	Lecture 25 : Dimensionality Reduction: PCA [Slides] [Slides] [Whiteboard] [Poll]	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).
Tue, 19-Apr			HW7 Solution Session
Wed, 20-Apr	Lecture 26 : K-Means / Ensemble Methods [Slides] [Whiteboard] [Poll]	A Brief Introduction to Boosting. Robert E. Schapire (1999).
Thu, 21-Apr	Lecture 26.5 : Recommender Systems (mini-lecture, video only) [Slides] [Whiteboard] [Video]	Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).	HW8 Due HW9 Out
Fri, 22-Apr	Recitation: HW9 [Handout] [Solutions]
Mon, 25-Apr	Lecture 27 : Exam 3 Review / Societal Impacts of ML [Slides] [Whiteboard] [Poll]	10601 Learning Objectives. Matt Gormley (2021).
Wed, 27-Apr	(No Lecture)		HW9 due (only two grace/late days permitted) Mock Exam 3 & Exam 3 practice problems out
Fri, 29-Apr	(No Recitation)		HW8 Solution Session
Sun, 1-May			HW9 Solution Session
Tue, 3-May	Exam 3 (Tuesday, May 3rd, 9:30am-11:30am) --details will be announced on Piazza

Introduction to Machine Learning

Important Notes

Tentative Schedule

Classification & Regression

Linear Models

Deep Learning

Learning Theory

Generative Models

Graphical Models

Reinforcement Learning

Learning Paradigms