Andrei Test - Courses lists

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Graduate Courses

Fall 2022

Theoretical Machine Learning
Can the mechanism of learning be automated and implemented by a machine? In this course we formally define and study various models that have been proposed for learning. The course presents and contrasts the statistical, computational and game-theoretic models for learning. Likely topics include: intro to statistical learning theory and generalization; learning in adversarial settings and the on-line learning model; using convex optimization to model and solve learning problems; learning with partial observability; boosting; reinforcement learning and control; introduction to the theory of deep learning.
Instructors: Elad Hazan
Foundations of Probabilistic Modeling
This course covers fundamental topics in probabilistic modeling and allows you to contribute to this important area of machine learning and apply it to your work. We learn how to model data arising from different fields and devise algorithms to learn the structure underlying these data for the purpose of prediction and decision making. We cover several model classes--including deep generative models--and several inference algorithms, including variational inference and Hamiltonian Monte Carlo. Finally, we cover evaluation methods for probabilistic modeling as well as tools to challenge our models' assumptions.
Instructors: Adji Bousso Dieng
Automated Reasoning about Software
An introduction to algorithmic techniques for reasoning about software. Basic concepts in logic-based techniques including model checking, invariant generation, symbolic execution, and syntax-guided synthesis; automatic decision procedures in modern solvers for Boolean Satisfiability (SAT) and Satisfiability Modulo Theory (SMT); and their applications in automated verification, analysis, and synthesis of software. Emphasis on algorithms and automatic tools.
Instructors: Zachary Kincaid
Advanced Computer Systems
COS 518 is a graduate course in computer systems. Its goals are: (1) To understand the core concepts of computer systems, rather than particular implementation details. (2) To understand the state of the art in distributed, storage, mobile, and operating systems. (3) To understand how to engage in cutting-edge systems research and development. This course assumes a basic familiarity with computer systems and networking concepts.
Instructors: Wyatt Lloyd
Advanced Algorithm Design
Broadly covers algorithmic design ideas of the past few decades, preparing students to understand current research papers in algorithms. Although designed for computer science grads, it may be suitable for advanced undergrads and non-CS grads as well. The course is thematically distinct from undergrad algorithms (such as COS 423) in its extensive use of ideas such as randomness, optimization, approximation, and high dimensional geometry, which are increasingly important in applications. The course also exposes students to modern algorithmic concerns, including dealing with uncertainty and strategic (i.e., game-theoretic) behaviors.
Instructors: Matt Weinberg, Huacheng Yu
Advanced Computer Vision
Advanced topics in computer vision, with a focus on recent methods and current research. Topics include 3D vision, recognition, and the intersection of computer vision and other fields. Appropriate for students who have taken COS 429 or related courses and would like further exposure to computer vision.
Instructors: Jia Deng
Introduction to Genomics and Computational Molecular Biology
This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).
Instructors: Joshua Akey, Claire McWhite, Mona Singh
Extramural Research Internship
One-term full time research internship at a host institution to perform scholarly research directly relevant to a student's dissertation work. Research objectives are determined by the student's advisor in consultation with the outside host. Monthly progress reports and a final paper are required. Enrollment limited to post-generals students. Students are permitted to enroll in this one-semester course at most twice. Participation is considered exceptional.
Instructors: Kyle Jamieson
Advanced Topics in Computer Science: Algorithmic Mechanism Design for Cryptocurrencies and DeFi
This course focuses on Algorithmic Mechanism Design, the design of algorithms that interact with strategic users, with a focus on applications to cryptocurrencies and Decentralized Finance. The initial portion of the course teaches background material on mechanism design for cryptocurrencies. The rest of the course involves in-depth presentations of recent research papers, and domain-specific material such as whitepapers, reports, etc.
Instructors: Mark Braverman, Matt Weinberg
Advanced Topics in Computer Science: Recent Advances in Computer Vision
Computer vision is a rapidly-evolving field, with technological innovations enabling societal impact, and societal needs fueling innovation. We select a few advanced computer vision topics to explore, focusing in particular on the robustness, transparency and fairness of computer vision systems. Students are expected to routinely read and present research papers, with special attention on developing excellent oral and written scientific communication skills
Instructors: Olga Russakovsky

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