10-301 + 10-601, Spring 2019

Important Notes

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

Lecture Videos

Panopto folder: [Andrew ID Required] 10-601 B/D
The above folders contains all the lecture video recordings for the specified section(s). To access them, please select Sign in using "Canvas".
We have also included links to individual videos below -- however, we recommend checking the link above to find the latest videos.

Tentative Schedule

Date	Lecture	Readings	Announcements
Classification
Mon, 14-Jan	Lecture 1 : Course Overview [Slides] [Video]	10601 Notation Crib Sheet. Matt Gormley (2018). 10601 Learning Objectives. Matt Gormley (2018). Visual Information Theory. Christopher Olah (2015). blog.
Wed, 16-Jan	Lecture 2 : Decision Trees [Slides] [Video]	Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.	HW1 out
Fri, 18-Jan	Recitation: HW1 [Video]
Mon, 21-Jan	(No Class: Martin Luther King Day)
Wed, 23-Jan	Lecture 3 : Decision Trees [Slides] [Video] [Poll]	Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.	HW2 out HW1 due
Fri, 25-Jan	Recitation: HW2 [Video]
Mon, 28-Jan	Lecture 4 : k-Nearest Neighbors [Slides] [Video] [Poll]	Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.
Wed, 30-Jan	Lecture 5 : Model Selection [Slides] [Video] [Poll]
Fri, 1-Feb	(No Recitation)
Mon, 4-Feb	Lecture 6 : Perceptron [Slides] [Video] [Poll]	The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.
Linear Models
Wed, 6-Feb	Lecture 7 : Linear Regression [Slides] [Video] [Poll]	Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.	HW3 out HW2 due
Fri, 8-Feb	Recitation: HW3 [Video]
Mon, 11-Feb	Lecture 8 : Optimization for ML [Slides] [Video] [Poll]	Estimating Probabilities: MLE and MAP. Tom Mitchell (2016, draft). Optimization lecture notes from 10-600. Geoff Gordon (2016).
Wed, 13-Feb	Lecture 9 : Logistic Regression [Slides] [Video] [Poll]	Logistic Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 1.4.6, 8.1-8.3, 8.6.
Fri, 15-Feb	(No recitation)		HW4 out HW3 due
Mon, 18-Feb	Lecture 10 : Midterm Exam Review / Multinomial Logistic Regression / Feature Engineering [Slides] [Video] [Poll]
Deep Learning
Wed, 20-Feb	Lecture 11 : Regularization / Neural Networks [Slides] [Video] [Poll]	Regularization for Deep Learning. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 7.1 and 7.8.
Thu, 21-Feb	Midterm Exam 1 (Evening Exam) -- details will be announced on Piazza
Fri, 22-Feb	Recitation: HW4 [Video]
Mon, 25-Feb	Lecture 12 : Neural Networks [Slides] [Video] [Poll]	Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.
Wed, 27-Feb	Lecture 13 : Backpropagation [Slides] [Video] [Poll]	Deep Feedforward Networks. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 6.5.
Fri, 1-Mar	Recitation: HW5 [Video]		HW5 out HW4 due
Mon, 4-Mar	Lecture 14 : Deep Learning [Slides] [Video] [Poll]	[Optional] Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.
Learning Theory
Wed, 6-Mar	Lecture 15 : Learning Theory: PAC Learning [Slides] [Video] [Poll]	Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.
Fri, 8-Mar	(No class: Mid-semester break)
Mon, 11-Mar	(No class: Spring break)
Wed, 13-Mar	(No class: Spring break)
Fri, 15-Mar	(No class: Spring break)
Mon, 18-Mar	Lecture 16 : Learning Theory: PAC Learning [Slides] [Video] [Poll]	A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).
Generative Models
Wed, 20-Mar	Lecture 17 : MLE/MAP [Slides] [Video] [Poll]	Estimating Probabilities: MLE and MAP. Tom Mitchell (2016, draft).
Fri, 22-Mar	Recitation: HW6 [Video]		HW6 out HW5 due
Graphical Models
Mon, 25-Mar	Lecture 18 : Naive Bayes [Slides] [Video] [Poll]	Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Tom Mitchell (2016, draft).
Wed, 27-Mar	Lecture 19 : Hidden Markov Models (Part I) [Slides] [Video] [Poll]	[Only pages 257 - 266] A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Rabiner (1989).
Fri, 29-Mar	Lecture 20 : Midterm Exam Review / Hidden Markov Models (Part II) [Slides] [Video] [Poll]		HW7 out HW6 due
Mon, 1-Apr	Recitation: HW7 [Video]
Wed, 3-Apr	Lecture 21 : Bayesian Networks [Slides] [Video] [Poll]	Directed Graphical Models (Bayes nets). Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 10.
Thu, 4-Apr	Midterm Exam 2 (Evening Exam) -- details will be announced on Piazza
Fri, 5-Apr	(No recitation)
Learning Paradigms
Mon, 8-Apr	Lecture 22 : Reinforcement Learning: Markov Decision Processes [Slides] [Video] [Poll]	Reinforcement Learning: A Survey. Kaelbling, et al (1996).
Wed, 10-Apr	Lecture 23 : Reinforcement Learning: Value/Policy Iteration [Slides] [Video] [Poll]		HW8 out HW7 due
Fri, 12-Apr	(No class: Spring Carnival)
Mon, 15-Apr	Lecture 24 : Reinforcement Learning: Q-Learning [Slides] [Video] [Poll]
Wed, 17-Apr	Lecture 25 : Deep Reinforcement Learning / K-Means [Slides] [Video] [Poll]	[Optional] Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).
Fri, 19-Apr	Recitation: HW8 [Video]
Mon, 22-Apr	Lecture 26 : Dimensionality Reduction: PCA [Slides] [Video] [Poll]	A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).
Wed, 24-Apr	Lecture 27 : SVMs / Kernel Methods [Slides] [Video] [Poll]	Kernel Methods. Hal Daumé III (2017). CIML, Chapter 11.	HW9 out HW8 due
Fri, 26-Apr	Recitation: HW9 [Video]
Mon, 29-Apr	Lecture 28 : Ensemble Methods / Recommender Systems [Slides] [Video] [Poll]	A Brief Introduction to Boosting. Robert E. Schapire (1999). Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).
Wed, 1-May	Lecture 29 : Final Exam Review [Slides] [Video] [Poll]	10601 Learning Objectives. Matt Gormley (2018).	HW9 due
Fri, 3-May	Recitation: Final Exam Review [Video]
6-May	Final Exam (1:00 PM - 4:00 PM) -- details will be announced on Piazza

Introduction to Machine Learning

Important Notes

Lecture Videos

Tentative Schedule

Classification

Linear Models

Deep Learning

Learning Theory

Generative Models

Graphical Models

Learning Paradigms