10-301 + 10-601, Spring 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
Wed, 18-Jan	Lecture 1 : Course Overview [Slides] [Slides (Inked)] [Whiteboard]	Command Line and File I/O Tutorial. 10601 Course Staff (2020). 10601 Learning Objectives. Matt Gormley (2023). Math Resources. 10601 Course Staff (2023).
Fri, 20-Jan	Background Test (in-class, required)		HW1 Out
Sat, 21-Jan	Recitation: HW1 (video recording only) [Handout] [Solutions] [Whiteboard] [Video]
Mon, 23-Jan	Lecture 2 : Machine Learning as Function Approximation [Slides] [Slides (Inked)] [Whiteboard]	10601 Notation Crib Sheet. Matt Gormley (2023).
Wed, 25-Jan	Lecture 3 : Decision Trees [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Visual Information Theory. Christopher Olah (2015). blog. Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.	HW1 Due HW2 Out
Fri, 27-Jan	Recitation: HW2 [Handout] [Solutions] [Whiteboard]
Mon, 30-Jan	Lecture 4 : k-Nearest Neighbors [Slides] [Slides (Inked)] [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 1-Feb	Lecture 5 : Model Selection and Experimental Design [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.
Linear Models
Fri, 3-Feb	Lecture 6 : Perceptron [Slides] [Slides (Inked)] [Whiteboard] [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.	HW2 Due HW3 Out
Sat, 4-Feb	Short Video: Decision Trees with Real-Valued Features [Whiteboard] [Video]
Mon, 6-Feb	Lecture 7 : Linear Regression [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.
Wed, 8-Feb	Recitation: HW3 [Handout] [Solutions] [Whiteboard]
Fri, 10-Feb	(No recitation)		HW3 due (only two grace/late days permitted) Exam 1 Practice Problems out
Mon, 13-Feb	Lecture 8 : Exam 1 Review / Optimization for ML [Slides] [Slides (Inked)] [Whiteboard] [Poll]	An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).
Tue, 14-Feb
Wed, 15-Feb	Lecture 9 : Stochastic Gradient Descent / Logistic Regression [Slides] [Slides (Inked)] [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, 16-Feb	Exam 1 (evening exam, details will be announced on Piazza)
Fri, 17-Feb	Recitation: HW4 [Handout] [Solutions] [Whiteboard]		HW4 Out
Mon, 20-Feb	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.
Deep Learning
Wed, 22-Feb	Lecture 11 : Neural Networks [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.
Fri, 24-Feb	Lecture 12 : Backpropagation [Slides] [Slides (Inked)] [Whiteboard] [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, 26-Feb			HW4 Due HW5 Out
Mon, 27-Feb	Lecture 13 : Deep Learning [Slides] [Slides (Inked)] [Whiteboard] [Poll]	[Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Wed, 1-Mar	Recitation: HW5 [Handout] [Solutions] [Whiteboard]
Fri, 3-Mar	Lecture 14 : Deep Learning [Slides] [Slides (Inked)] [Poll]	Numpy Notes. 10601 Course Staff (2021). Logging Tutorial. 10601 Course Staff (2021). Workflow and Debugging Slides. 10601 Course Staff (2021).
Mon, 6-Mar	(Spring Break - No Class)
Tue, 7-Mar
Wed, 8-Mar	(Spring Break - No Class)
Thu, 9-Mar
Fri, 10-Mar	(Spring Break - No Class)
Learning Theory
Mon, 13-Mar	Lecture 15 : PAC Learning [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Tue, 14-Mar
Wed, 15-Mar	Lecture 16 : PAC Learning / MLE+MAP [Slides] [Slides (Inked)] [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. A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).	Exit Poll: Exam 1 due
Thu, 16-Mar
Fri, 17-Mar	(No Recitation)		HW5 Due HW6 Out
Mon, 20-Mar	Lecture 17 : Naive Bayes [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Tom Mitchell (2016, draft).
Wed, 22-Mar	Recitation: HW6 [Handout] [Solutions]
Graphical Models
Fri, 24-Mar	Lecture 18 : Exam 2 Review / Hidden Markov Models (Part I) [Slides] [Slides (Inked)] [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) Exam 2 Practice Problems out
Mon, 27-Mar	Lecture 19 : Hidden Markov Models (Part II) [Slides] [Slides (Inked)] [Whiteboard] [Poll]	[Only pages 257 - 266] A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Rabiner (1989).
Wed, 29-Mar	Lecture 20 : Bayesian Networks [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Directed Graphical Models (Bayes nets). Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 10.
Thu, 30-Mar	Exam 2 (evening exam, details will be announced on Piazza)
Fri, 31-Mar	Recitation: HW7 [Handout] [Solutions]		HW7 Out
Reinforcement Learning
Mon, 3-Apr	Lecture 21 : Reinforcement Learning: MDPs [Slides] [Slides (Inked)] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 5-Apr	Lecture 22 : Reinforcement Learning: Value/Policy Iteration [Slides] [Slides (Inked)] [Whiteboard] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Fri, 7-Apr	Lecture 23 : Reinforcement Learning: Q-Learning / Deep RL [Slides] [Slides (Inked)] [Poll]	[Optional] Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).
Mon, 10-Apr	Recitation: HW8 [Handout] [Solutions]		HW7 Due HW8 Out
Learning Paradigms
Wed, 12-Apr	Lecture 24 : Dimensionality Reduction: PCA [Slides] [Slides (Inked)] [Whiteboard] [Poll]	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).
Fri, 14-Apr	(Spring Carnival - No class)
Mon, 17-Apr	Lecture 25 : 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, 19-Apr	Lecture 26 : Ensemble Methods: Boosting / Recommender Systems [Slides] [Slides (Inked)] [Whiteboard] [Poll]	A Brief Introduction to Boosting. Robert E. Schapire (1999). Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).
Fri, 21-Apr	Recitation: HW9 [Handout] [Solutions]		HW8 Due HW9 Out
Mon, 24-Apr	Lecture 27 : Exam 3 Review [Slides] [Poll]
Wed, 26-Apr	Lecture 28 : Special Topics: ChatGPT / Significance Testing for ML / Societal Impacts of ML [Slides] [Slides (Inked)] [Poll]	10601 Learning Objectives. Matt Gormley (2023).
Thu, 27-Apr			HW9 due (only two grace/late days permitted) Exam 3 Practice Problems out
Fri, 28-Apr	(No Recitation)
Tue, 2-May	Exam 3 (5:30pm-7:30pm -- details will be announced on Piazza)

Introduction to Machine Learning

Important Notes

Tentative Schedule

Classification & Regression

Linear Models

Deep Learning

Learning Theory

Graphical Models

Reinforcement Learning

Learning Paradigms