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

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Event Type	Date	Description	Readings	Course Materials
Lecture 1	Thursday Jan 9	What is machine learning? Course overview and an orientation to the major branches of machine learning: supervised, unsupervised, and reinforcement learning	None	[slides]
		Module 1: Supervised Learning
	Tuesday Jan 14	NO CLASS Make up on Friday 1/17
Lecture 2	Thursday 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	Friday Jan 17	How flexible should my algorithms be? The bias-variance tradeoff The bias-variance tradeoff explained using K-nearest neighbors classification	ISL 2.2	[slides]
	Monday Jan 20	Martin Luther King Jr. Day
Lecture 4	Tuesday Jan 21	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]
Deliverable	Wednesday Jan 22	Assignment #1 Due (at 9pm) Probability, Linear Algebra, & Computational Programming		[assignment] [submit]
Lecture 5	Thursday Jan 23	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 28	Performance evaluation: metrics for regression/classification 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 January 30	Experimental designs for evaluating generalization performance and model comparison How to construct effective experimental designs for evaluating and comparing models; using model performance metrics to measure 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]
Lecture 8	Tuesday Feb 4	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]
Deliverable	Wednesday Feb 5	Assignment #2 Due (at 9pm) Supervised Machine Learning Fundamentals		[assignment] [submit]
Lecture 9	Thursday Feb 6	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 11	Generative models for classification Generative vs discriminative models; naïve Bayes	ISL 4.4 and 4.5	[slides]
Lecture 11	Thursday Feb 13	Tree-based models and ensembles From decision trees to random forests: bagging, bootstrapping, and boosting	ISL 8.1 and 8.2	[slides]
Lecture 12	Tuesday Feb 18	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 13	Thursday Feb 20	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]
Deliverable	Friday Feb 19 Feb 21	Assignment #3 Due (at 9pm) Supervised learning model training and evaluation		[assignment] [submit]
Lecture 14	Tuesday Feb 25	Introduction to Deep learning I Common architectures of deep learning models and the tools used to implement them	UDL Ch 10: Convolutional Neural Networks	[slides]
Lecture 15	Thursday Feb 27	Introduction to Deep learning II Common architectures of deep learning models and the tools used to implement them	DL Ch 11: Practical Methodology UDL Ch 12.1-12.5: Transformers	[slides]
		Module 2: Unsupervised Learning
Lecture 16	Tuesday Mar 4	Dimensionality reduction The Curse of Dimensionality and intro to principal components analysis (PCA)	ISL 6.3, 6.4, 12.1, and 12.2	[slides]
Lecture 17	Thursday Mar 6	Principal components analysis (PCA) Explaining how PCA works and how we calculate the principal components.	ISL 12.4	[slides]
Deliverable	Thursday Mar 6	Assignment #4 Due (at 9pm) Neural Networks		[submit] [assignment] [NN Math Guide]
Deliverable	Friday Mar 7	Project Proposal Due (at 9pm)		[project] [submit]
	Mar 8-16	Spring Break Week
Lecture 18	Tuesday Mar 18	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 20	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 25	Reinforcement Learning I Formulating the reinforcement learning problem	RL Ch 1: 1.1-1.6; Ch 2: 2.1-2.5	[slides]
Deliverable	Wednesday Mar 26	Assignment #5 Due (at 9pm) Kaggle Competition and Unsupervised Learning Kaggle Competition Ends 9pm on Tuesday Mar 25		[assignment] [submit]
Lecture 21	Thursday Mar 27	Reinforcement Learning II Policy and value functions, rewards, and introduction to Markov processes	RL Ch 3	[slides]
Lecture 22	Tuesday Apr 1	Reinforcement Learning III From Markov Chains to Markov Decision Processes (MDPs)	RL Ch 4	[slides]
Lecture 23	Thursday Apr 3	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: Practical Considerations and Advanced Topics
Lecture 24	Tuesday Apr 8	Practical Considerations and Advanced Topics I A survey of practical considerations and advanced topics	None	[slides]
Lecture 25	Thursday Apr 10	Practical Considerations and Advanced Topics II A survey of practical considerations and advanced topics	None	[slides]
Lecture 26	Tuesday Apr 15	Practical Considerations and Advanced Topics III A survey of practical considerations and advanced topics	None	[slides]
Deliverable	Wednesday Apr 16	Draft Final Project Report Due (at 9pm)		[project] [submit]
Deliverable	Wednesday Apr 16	(Optional) Assignment #6 Due (at 9pm) Reinforcement learning		[assignment]
Deliverable	Wednesday Apr 30 9am-noon	Final project showcase Meets during the final exam period Due on this day: Final Project Report (due by 9am on GradeScope) Final Project Presentation (due by 9am; submit link to presentation here) Project Github Repository (due by 9am on GradeScope) Final Project Peer Evaluation (due by 9pm; submit via TEAMMATES, see email)		[project]