Schedule

Spring 2025

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.

Key to books used below

• ISL = An Introduction to Statistical Learning with Python, by James, Witten, Hastie, and Tibshirani
• UDL = Understanding Deep learning by Simon Prince
• 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: An Introduction, by Sutton and Barto

Event Type	Date	Description	Readings	Course Materials
Lecture 1	Thursday Jan 11	What is machine learning? Course overview and an orientation to the major branches of machine learning: supervised, unsupervised, and reinforcement learning	None	[slides]
	Monday Jan 15	Martin Luther King Jr. Day
		Module 1: Supervised Learning
Lecture 2	Tuesday Jan 16	An end-to-end machine learning example An introduction to formulating a supervised machine learning problem. Stating the problem, creating the model, evaluating performance, and operationalizing the solution.	ISL Ch. 1 + 2.1 Watch this lecture	[slides] [sample code]
Lecture 3	Thursday Jan 18	How flexible should my algorithms be: the bias-variance tradeoff K-nearest neighbors classification and the bias-variance tradeoff	ISL 2.2	[slides]
Deliverable	Monday Jan 22	Assignment #1 Due (at 9pm) Probability, Linear Algebra, & Computational Programming		[assignment] [sample Q12] [submit]
Lecture 4	Tuesday Jan 23	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	Thursday Jan 25	Linear Models II Nonlinear transformations of predictors; linear models for classification including the perceptron and logistic regression; cost/loss functions for classification (cross entropy loss); introduction to gradient descent.	ISL 3.3 and 3.5	[slides]
Lecture 6	Tuesday Jan 30	Performance evaluation and model comparison Choosing the right model: accuracy vs speed vs interpretability; metrics for supervised learning performance evaluation: types of errors, receiver operating characteristics curves, and confusion matrices	ISL 4.1, 4.2, and 4.3	[slides]
Lecture 7	Thursday Feb 1	Resampling methods for performance evaluation: model validation and testing strategies How to use model performance metrics to measure metrics of generalization performance; resampling techniques: training, testing, and validation datasets and cross validation; common pitfalls around biased sampling and data snooping/leakage	ISL 5.1 and 5.2	[slides]
Deliverable	Monday Feb 5	Assignment #2 Due (at 9pm) Supervised Machine Learning Fundamentals		[assignment] [submit]
Lecture 8	Tuesday Feb 6	Decision theory A risk-based framework for determining to operate supervised learning algorithms in practice; choosing ROC operating points through risk-minimization and how application-specific costs associated with different types of errors can be used to determine optimal operating points for classifiers	Link to reading	[slides]
Lecture 9	Thursday Feb 8	Reducing overfit Feature selection; Occam’s razor; Subset selection; L1 (ridge), L2 (LASSO), and elastic net regularization; early stopping.	ISL 6.1 and 6.2	[slides]
Lecture 10	Tuesday Feb 13	Generative models for classification Generative vs discriminative models; linear discriminant analysis, quadratic discriminant analysis, and naïve Bayes	ISL 4.4 and 4.5	[slides]
Lecture 11	Thursday Feb 15	Tree-based models and ensembles From decision trees to random forests: bagging, bootstrapping, and boosting	ISL 8.1 and 8.2	[slides]
Deliverable	Monday Feb 19	Assignment #3 Due (at 9pm) Supervised learning model training and evaluation		[assignment] [submit]
Lecture 12	Tuesday Feb 20	Kernel Methods Introducing Kernel machines via the kernel perceptron, maximum margin classifiers, and support vector machines	ISL Ch 9: 9.1-9.4	[slides]
Lecture 13	Thursday Feb 22	Neural networks I Introduction to neural networks and representation learning; forward propagation, network architecture, and how to adapt to regression or classification problems	UDL Ch 3: 3.1, 3.2; PRML Ch 5: 5.1	[slides]
Lecture 14	Tuesday Feb 27	Neural networks II Fitting a neural network to training data through gradient descent and backpropagation; how backpropagation is used to compute gradients in neural networks; hyperparameters and architecture choices in neural networks and practices for training neural networks warningfully	UDL Ch 6: 6.1-6.2.2; PRML Ch 5: 5.3.1, and Calculus on Computational Graphs	[slides]
Lecture 15	Thursday Feb 29	Introduction to Deep learning Common architectures of deep learning models, in particular convolutional neural networks for computer vision and the tools used to implement them	DL Ch 11: Practical Methodology	[slides]
		Module 2: Unsupervised Learning
Lecture 16	Tuesday Mar 5	Dimensionality reduction The Curse of Dimensionality and intro to principal components analysis (PCA)	ISL 6.3, 6.4, 12.1, and 12.2	[slides]
Deliverable	Wednesday Mar 6	Assignment #4 Due (at 9pm) Neural Networks		[assignment] [submit] [NN Math Guide]
Lecture 17	Thursday Mar 7	Principal components analysis (PCA) Explaining how PCA works and how we calculate the principal components.	ISL 12.4	[slides]
Deliverable	Friday Mar 8	Project Proposal Due (at 9pm)		[project] [submit]
	Mar 9-16	Spring Break Week
Lecture 18	Tuesday Mar 19	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 19	Thursday Mar 21	Clustering II Hierarchical clustering, DBSCAN, and spectral clustering	DM Ch 7 (link): 7.3 and 7.4	[slides]
		Module 3: Reinforcement Learning
Lecture 20	Tuesday Mar 26	Reinforcement Learning I Formulating the reinforcement learning problem	RL Ch 1: 1.1-1.6; Ch 2: 2.1-2.5	[slides]
Lecture 21	Thursday Mar 28	Reinforcement Learning II Policy and value functions, rewards, and introduction to Markov processes	RL Ch 3	[slides]
Deliverable	Sunday Mar 31	Assignment #5 Due (at 9pm) Kaggle Competition and Unsupervised Learning Kaggle Competition Ends 9pm on Saturday Mar 30		[assignment] [submit]
Lecture 22	Tuesday Apr 2	Reinforcement Learning III From Markov Chains to Markov Decision Processes (MDPs)	RL Ch 4	[slides]
Deliverable	Wednesday Apr 3	Draft Final Project Report Due (at 9pm)		[project] [submit]
Lecture 23	Thursday Apr 4	Reinforcement Learning IV Finding optimal policies through policy iteration, value iteration, and Monte Carlo methods	RL Ch 5: 5.1-5.3	[slides]
		Module 4: Machine Learning Trends, Practical Considerations, and Advanced Topics
Lecture 24	Tuesday Apr 11	Advanced topics and applications I A survey of advanced topics including semi- and self-supervised learning	None	[slides]
Lecture 25	Thursday Apr 9	Advanced topics and applications II Discussion on where the field is heading and how to stay up-to-date	None	[slides]
Deliverable	Tuesday Apr 16	Final project showcase (last class meeting of the semester)		[project]
Deliverable	Sunday Apr 21	Final Project Report Due (at 9pm)		[project] [submit]
Deliverable	Monday Apr 22	Final Project Peer Evaluation Due (at 9pm)		[project] [submit via TEAMMATES (see email)]
Deliverable	Monday Apr 22	Final Project Repository Due (at 9pm)		[project] [submit]
Deliverable	Wednesday Apr 24	(Optional) Assignment #6 Due (at 9pm) Reinforcement learning		[assignment] [submit]