Curriculum

DATA C200: Principles and Techniques of Data Science

This course explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for Machine Learning (ML) methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing.

Course Format: Large Lecture + Discussion Section

This course focuses on the theory of probability and statistical inference required for data science, machine learning and Artificial Intelligence (AI). Topics include conditioning and Bayes methods, maximum likelihood, asymptotics as well as finite sample bounds, measures of distance between distributions, empirical distributions and nonparametric methods, and multiple testing.

Course Format: Lecture + Discussion Section

Select either COMPSCI 289A or STAT 254 (subject to course availability).

COMPSCI 289A: Introduction to Machine Learning

This course provides an introduction to theoretical foundations, algorithms and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming language.

Course Format: Large Lecture + Discussion Section

STAT 254: Modern Statistical Prediction and Machine Learning

This course is about statistical learning methods and their use for data analysis. Upon completion, students will be able to build baseline models for real-world data analysis problems, implement models using programming languages and draw conclusions from models. The course will cover principled statistical methodology for basic machine learning tasks such as regression, classification, dimension reduction and clustering. Methods discussed will include linear regression, subset selection, ridge regression, LASSO, logistic regression, kernel smoothing methods, tree-based methods, bagging and boosting, neural networks, Bayesian methods, as well as inference techniques based on resampling, cross-validation and sample splitting.

Course Format: Lecture + Discussion Section

This course will equip students with essential skills in communication, public speaking and networking. Topics will include workplace communication, storytelling, negotiation, crafting effective presentations, and job search strategies. Students will also engage with guest speakers from industry to better understand current expectations and trends.

Course Format: Small Workshop-Based

This independent study course will afford students a structured opportunity to prepare for the program’s comprehensive examination. It will reinforce key concepts from the core curriculum of the program and provide students with additional opportunities to ask questions and review material.

Course Format: Small Workshop-Based

Applied Case Studies and Responsible AI Seminar

This seminar will explore real-world applications of AI and ML through a series of in-depth case studies drawn from diverse domains. Cases will provide students with opportunities to critically assess sampling and data pipelines, modeling, implementation, the reliability of conclusions drawn and societal impact. Students will learn how to identify the potential and limits of machine learning and AI in real-world settings. The majority of case studies presented will include ethical or philosophical considerations. Topics will span transparency, fairness, equity, privacy, safety, security and accountability.

COMPSCI 188: Introduction to Artificial Intelligence

This course explores ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception and language understanding.

Course Format: Large Lecture + Discussion Section

COMPSCI 280A: Intro to Computer Vision and Computational Photography

This course introduces students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical to contemporary, with an emphasis on using these techniques to build practical systems. The hands-on emphasis will be reflected in the programming assignments, where students will acquire their own images and develop, largely from scratch, image analysis and synthesis tools for real-world applications.

Course Format: Lecture + Discussion Section

COMPSCI 282A: Designing, Visualizing and Understanding Deep Neural Networks

Deep Networks have revolutionized computer vision, language technology, robotics and control. They have a growing impact in many other areas of science and engineering. They do not, however, follow a closed or compact set of theoretical principles. In Yann Lecun's words, they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.

Course Format: Lecture + Discussion Section

COMPSCI 285: Deep Reinforcement Learning, Decision Making, and Control

Intersection of control, reinforcement learning and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations (e.g., computer vision, speech recognition and natural language processing). Advanced treatment of the reinforcement learning formalism, the most critical model-free reinforcement learning algorithms (policy gradients, value function and Q-function learning and actor-critic), a discussion of model-based reinforcement learning algorithms, an overview of imitation learning and a range of advanced topics (e.g., exploration, model-based learning with video prediction, transfer learning, multi-task learning and meta-learning).

Course Format: Lecture + Discussion Section

COMPSCI 288: Natural Language Processing

This course focuses on methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering and computational linguistics techniques.

Course Format: Lecture + Discussion Section

DATA C204: Human Contexts and Ethics of Data

This course teaches you to use approaches from across the humanities and interpretive social sciences and tools of Science, Technology, and Society (STS) to recognize, analyze and shape the human contexts, social implications, and ethics of data and data technologies, including data analytics, algorithmic decision systems, ML, and AI.

Course Format: Lecture + Discussion Section

EECS 227AT - Optimization Models in Engineering

This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization.

Course Format: Lecture + Discussion Section

STAT 210A: Theoretical Statistics

An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation.

Course Format: Lecture + Discussion Section

STAT 215A: Applied Statistics and Machine Learning

This course will focus on applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods, and the match between the assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods.

Course Format: Lecture + Discussion Section

STAT 238: Bayesian Statistics

This course will focus on Bayesian methods and concepts: conditional probability, one-parameter and multiparameter models, prior distributions, hierarchical and multi-level models, predictive checking and sensitivity analysis, model selection, linear and generalized linear models, multiple testing and high-dimensional data, mixtures, non-parametric methods. Case studies of applied modeling. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Basic theory for Bayesian methods and decision theory. The selection of topics may vary from year to year.

Course Format: Lecture + Discussion Section

STAT 248: Analysis of Time Series 

Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building. 

Course Format: Lecture + Discussion Section

STAT 256: Causal Inference

This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields, including political science, economics, sociology, public health and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision- and policy-making.

Course Format: Lecture + Discussion Section

STAT 259: Reproducible and Collaborative Statistical Data Science 

A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash, Git, Python, and LaTeX. 

Course Format: Lecture + Discussion Section

STAT 265: Forecasting

Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity and power laws.

Course Format: Lecture + Discussion Section