IDS 705: Principles of Machine Learning

Schedule and Syllabus

The schedule below is a guide to what we will be covering throughout the semester and is subject to change to meet the learning goals of the class. Check this website regularly for the latest schedule and for course materials that will be posted here through links on the syllabus.
ISL = Introduction to Statistical Learning, by James, Witten, Hastie, and Tibshirani
DM = Introduction to Data Mining, by Tan, Steinbach, Karpatne, and Kumar
PRML = Pattern Recognition and Machine Learning, by Bishop
DL = Deep Learning, by Goodfellow, Bengio, and Courville
RL = Reinforcement Learning: An Introduction, by Sutton and Barto

Event Type	Date	Description	Readings	Course Materials
Lecture 1	Wednesday Jan 8	What is machine learning? Course overview and an orientation to the major branches of machine learning: unsupervised, supervised, and reinforcement learning	None	[slides]
Lecture 2	Monday Jan 13	An end-to-end machine learning example Stating the problem, creating the model, evaluating performance, and operationalizing the solution.	ISL Ch. 1 + 2.1	[slides] [sample code]
Lecture 3	Wednesday Jan 16	How flexible should my algorithms be: the bias-variance tradeoff K-nearest neighbors classification and the bias-variance tradeoff	ISL 2.2	[slides]
No class	Monday Jan 20	Martin Luther King, Jr. Day
Deliverable	Wednesday Jan 22	Assignment #1 Due		[assignment]
Lecture 4	Wednesday Jan 22	Linear Models I Simple linear regression, multiple linear regression, measuring error, model fitting and least squares, comparing linear regression and classification	ISL Intro of 3, 3.1, and 3.2	[slides]
Lecture 5	Monday Jan 27	Linear Models II Nonlinear transformations of predictors, cost/loss functions, selecting parameters through gradient descent.	ISL 3.3 and 3.5	[slides]
Lecture 6	Wednesday Jan 29	Performance evaluation and model comparison Choosing the right mode: accuracy vs speed vs interpretability; metrics for supervised learning performance evaluation: types of errors, receiver operating characteristics curves, confusion matrices	ISL 4.1, 4.2, and 4.3	[slides]
Lecture 7	Monday Feb 3	Validation and model testing Resampling techniques: training, testing, and validation datasets, the importance of ensuring representative resampling, and cross validation	ISL 5.1 and 5.2	[slides]
Deliverable	Wednesday Feb 5	Assignment #2 Due		[assignment]
Lecture 8	Wednesday Feb 5	Decision theory How to operate supervised learning algorithms in practice	Link to reading	[slides]
Lecture 9	Monday Feb 10	Reducing overfit Model and feature selection; Occam’s razor; Subset selection; L1 (ridge), L2 (LASSO), and elastic net regularization.	ISL 6.1 and 6.2	[slides]
Lecture 10	Wednesday Feb 12 Class starts 10 min late	Additional classification methods Linear discriminant analysis and naïve Bayes	ISL 4.4 and 4.5	[slides]
Lecture 11	Monday Feb 17	Tree-based models and ensembles From decision trees to random forests: bagging, bootstrapping, and boosting	ISL 8.1 and 8.2	[slides]
Deliverable	Wednesday Feb 19	Assignment #3 Due		[assignment]
Lecture 12	Wednesday Feb 19	Dimensionality reduction The Curse of Dimensionality and intro to principal components analysis (PCA)	ISL 6.3, 6.4, 10.1, and 10.2	[slides]
Lecture 13	Monday Feb 24	Principal components analysis (PCA) Explaining how PCA works and how we calculate the principal components.	ISL 10.3	[slides]
Deliverable	Wednesday Feb 26	End of Kaggle Competition		[kaggle competition]
Lecture 14	Wednesday Feb 26	Clustering I From K-means to Gaussian mixture model clustering and Expectation Maximization	DM Ch 7 (link): Intro, 7.1 and 7.2	[slides]
Lecture 15	Monday Mar 2	Clustering II Hierarchical clustering, DBSCAN, and spectral clustering	DM Ch 7 (link): 7.3 and 7.4	[slides]
Deliverable	Wednesday Mar 4	Kaggle Competition Reports Due		[kaggle report]
Lecture 16	Wednesday Mar 4	Neural networks I How a neural network works	PRML Ch 5: 5.1	[slides]
Deliverable	Friday Mar 6	Kaggle Team Peer Evaluation		[peer evaluation]
No class	Mar 9-20	Extended Spring break week
Lecture 17	Monday Mar 23	Neural networks II Backpropagation	PRML Ch 5: 5.3 (intro), 5.3.1, 5.3.2, and Calculus on Computational Graphs	[slides]
Lecture 18	Wed Mar 25	Introduction to Deep learning Implementing deep learning models in Keras	DL Ch 11: Practical Methodology	[slides]
Deliverable	Mon Mar 30	Final Project Proposal		[Final Project]
Lecture 19	Monday Mar 30	Reinforcement Learning I Formulating the reinforcement learning problem	RL Ch 1: 1.1-1.6; Ch 2: 2.1-2.5	[slides]
Lecture 20	Wednesday Apr 1	Reinforcement Learning II Policy and value functions, rewards, and introduction to Markov processes	RL Ch 3	[slides]
Deliverable	Monday Apr 6	Assignment #4 Due		[assignment] [neural network math tutorial]
Lecture 21	Monday Apr 6	Reinforcement Learning III From Markov Chains to Markov Decision Processes (MDPs)	RL Ch 4	[slides]
Lecture 22	Wednesday Apr 8	Reinforcement Learning IV Finding optimal policies through policy iteration, value iteration, and Monte Carlo methods	RL Ch 5: 5.1-5.3	[slides]
Lecture 25	Monday Apr 13	State-of-the-art machine learning and applications Cutting-edge applications and techniques: ideas on where the field is heading and how to stay up-to-date	None	[slides]
Deliverable	Wednesday Apr 15	Final project showcase and competition (last class meeting of the semester)		[Final Project]
Deliverable	Saturday Apr 18	Final Report		[Final Project]
Deliverable	Monday Apr 20	Final Project Peer Evaluation		[peer evaluation]
Optional Lecture A	Recorded	Kernel Smoothing Non-parametric methods including kernel density estimation, kernel regression, and local regression	None	[slides]
Optional Lecture B	Recorded	Kernel Methods Introducing Kernel machines via the kernel perceptron, maximum margin classifiers, and support vector machines	Optional: ISL Ch 9: 9.1-9.4	[slides]
OPTIONAL Deliverable	Thursday Apr 23	Assignment #5 Due		[assignment]