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, 13-Jan	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, 15-Jan	Lecture 2 : Machine Learning as Function Approximation [Slides] [Slides (Inked)]	10601 Notation Crib Sheet. Matt Gormley (2023).
Fri, 17-Jan	Recitation: HW1 [Handout] [Solutions]
Mon, 20-Jan	(MLK Day - No Class)
Wed, 22-Jan	Lecture 3 : Decision Trees [Poll]	Visual Information Theory. Christopher Olah (2015). blog. Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.	HW1 Due HW2 Out
Fri, 24-Jan	Recitation: HW2
Mon, 27-Jan	Lecture 4 : k-Nearest Neighbors [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 29-Jan	Lecture 5 : Model Selection and Experimental Design [Poll]	Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.
Linear Models
Fri, 31-Jan	Lecture 6 : Perceptron [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.
Mon, 3-Feb	Lecture 7 : Linear Regression [Poll]	Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.	HW2 Due HW3 Out
Wed, 5-Feb	Lecture 8 : Optimization for ML [Poll]	An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).
Fri, 7-Feb	Recitation: HW3		Exam 1 Practice Problems out
Mon, 10-Feb	Lecture 9 : Stochastic Gradient Descent / Logistic Regression [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, 12-Feb	Lecture 10 : Feature Engineering / Regularization [Poll]	Regularization for Deep Learning. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 7.1 and 7.8.
Fri, 14-Feb	Exam 1 Review OH
Neural Networks
Mon, 17-Feb	Lecture 11 : Neural Networks [Poll]	Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.
Mon, 17-Feb	Exam 1 (evening exam, details will be announced on Piazza)		HW4 Out
Wed, 19-Feb	Lecture 12 : Backpropagation I [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, 21-Feb	Recitation: HW4
Mon, 24-Feb	Lecture 13 : Backpropagation II [Poll]	Numpy Notes. 10601 Course Staff (2021). Logging Tutorial. 10601 Course Staff (2021). Workflow and Debugging Slides. 10601 Course Staff (2021).
Societal Impacts
Wed, 26-Feb	Lecture 14 : Societal Impacts of ML [Poll]		HW4 Due HW5 Out
Fri, 28-Feb	Recitation: HW5
Mon, 3-Mar	Spring break
Tue, 4-Mar
Wed, 5-Mar	Spring break
Thu, 6-Mar
Fri, 7-Mar	Spring break
Learning Theory
Mon, 10-Mar	Lecture 15 : PAC Learning [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Wed, 12-Mar	Lecture 16 : PAC Learning [Poll]	A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).
Fri, 14-Mar	Recitation: HW6
Sun, 16-Mar			HW5 Due HW6 Out, Exam 2 Practice Problems out
Deep Learning
Mon, 17-Mar	Lecture 17 : CNNs and RNNs [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Wed, 19-Mar	Lecture 18 : RNN-LMs and Transformers-LMs [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Fri, 21-Mar	Exam 2 Review OH
Sat, 22-Mar			HW6 Due (only two grace/late days permitted)
Mon, 24-Mar	Lecture 19 : AutoDiff, Pre-training, Fine-Tuning, In-context Learning [Poll]	Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Reinforcement Learning
Wed, 26-Mar	Lecture 20 : Reinforcement Learning: MDPs [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 26-Mar	Exam 2 (evening exam, details will be announced on Piazza)		HW7 Out
Fri, 28-Mar	Recitation: HW7
Mon, 31-Mar	Lecture 21 : Reinforcement Learning: Value/Policy Iteration [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 2-Apr	Lecture 22 : Reinforcement Learning: Q-Learning / Deep RL [Poll]	Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).
Fri, 4-Apr	(Carnival - No Recitation) April 03-05
Mon, 7-Apr	Recitation: HW8	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).
Tue, 8-Apr			HW7 Due HW8 Out
Learning Paradigms
Wed, 9-Apr	Lecture 23 : Recommender Systems / Ensemble Methods: Boosting [Poll]	A Brief Introduction to Boosting. Robert E. Schapire (1999). Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).
Fri, 11-Apr	(No recitation)
Mon, 14-Apr	Lecture 24 : Ensemble Methods: Bagging / K-Means [Poll]	k-means++: The Advantages of Careful Seeding. Arthur & Vassilvitskii (2006). Ensemble Methods. Hal Daumé III (2017). CIML, Chapter 13.
Wed, 16-Apr	Lecture 25 : Dimensionality Reduction: PCA [Poll]	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).	HW8 Due HW9 Out
Fri, 18-Apr	Recitation: HW9		Exam 3 Practice Problems out
Mon, 21-Apr	Lecture 26 : Special Topics: Generative Models for Vision / Significance Testing for ML [Poll]
Wed, 23-Apr	Lecture 27 : Special Topics: Bayesian Methods for Machine Learning [Poll]	10601 Learning Objectives. Matt Gormley (2023).
Thu, 24-Apr			HW9 Due (only two grace/late days permitted)
Fri, 25-Apr	Exam 3 Review OH
Apr-28 to May-04	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