More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. Probabilistic Machine Learning: An Introduction; by Kevin Patrick Murphy, MIT Press, 2021. ), Zhuokai: Mondays 11am to 12pm, Location TBD. In addition to his research, Veitch will teach courses on causality and machine learning as part of the new data science initiative at UChicago. This course is a basic introduction to computability theory and formal languages. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Mathematical Logic I-II. 100 Units. The new paradigm of computing, harnessing quantum physics. | Learn more about Rohan Kumar's work experience, education . Kernel methods and support vector machines Foundations of Machine Learning. Topics include DBMS architecture, entity-relationship and relational models, relational algebra, concurrency control, recovery, indexing, physical data organization, and modern database systems. Matlab, Python, Julia, R). Two new projects will test out ways to make "intelligent" water [] Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. CMSC25610. Application: text classification, AdaBoost Please sign up for the waitlist (https://waitlist.cs.uchicago.edu/) if you are looking for a spot. Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) Prerequisite(s): CMSC 20300 or CMSC 20600 or CMSC 21800 or CMSC 22000 or CMSC 22001 or CMSC 23000 or CMSC 23200 or CMSC 23300 or CMSC 23320 or CMSC 23400 or CMSC 23500 or CMSC 23900 or CMSC 25025. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Topics include: algebraic datatypes, an elegant language for describing and manipulating domain-specific data; higher-order functions and type polymorphism, expressive mechanisms for abstracting programs; and a core set of type classes, with strong connections to category theory, that serve as a foundational and practical basis for mixing pure functions with stateful and interactive computations. Prerequisite(s): CMSC 20300 Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. 100 Units. Instructor(s): Chenhao TanTerms Offered: Winter Ethics, Fairness, Responsibility, and Privacy in Data Science. No experience in security is required. Digital fabrication involves translation of a digital design into a physical object. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. Students who place out of CMSC14400 Systems Programming II based on the Systems Programming Exam must replace it with an additional elective, A core theme of the course is "generalization"; ensuring that the insights gleaned from data are predictive of future phenomena. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. 100 Units. Topics include (1) Statistical methods for large data analysis, (2) Parallelism and concurrency, including models of parallelism and synchronization primitives, and (3) Distributed computing, including distributed architectures and the algorithms and techniques that enable these architectures to be fault-tolerant, reliable, and scalable. Terms Offered: Autumn In this hands-on, practical course, you will design and build functional devices as a means to learn the systematic processes of engineering and fundamentals of design and construction. In total, the Financial Mathematics degree requires the successful completion of 1250 units. Honors Introduction to Computer Science I. Introduction to Computer Graphics. STAT 37500: Pattern Recognition (Amit) Spring. Professor, Departments of Computer Science and Statistics, Assistant Professor, Department of Computer Science, Edward Carson Waller Distinguished Service Professor Emeritus, Departments of Computer Science and Linguistics, Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science, Assistant Professor, Department of Computer Science, College, Assistant Professor, Computer Science (starting Fall 2023), Associate Professor, Department of Computer Science, Associate Professor, Departments of Computer Science and Statistics, Associate Professor, Toyota Technological Institute, Professor, Toyota Technological Institute, Assistant Professor, Computer Science and Data Science, Assistant Professor, Toyota Technological Institute. Introduction to Bioinformatics. Applications: recommender systems, PageRank, Ridge regression Instructor(s): S. LuTerms Offered: Autumn 5747 South Ellis Avenue Reviewer 1 Report. We expect this option to be attractive to a fair number of students from every major at UChicago, including the humanities, social sciences and biological sciences.. Information about your use of this site is shared with Google. Data-driven models are revolutionizing science and industry. Introduction to Robotics gives students a hands-on introduction to robot programming covering topics including sensing in real-world environments, sensory-motor control, state estimation, localization, forward/inverse kinematics, vision, and reinforcement learning. Instructor(s): Blase UrTerms Offered: Autumn MIT Press, Second Edition, 2018. Final: Wednesday, March 13, 6-8pm in KPTC 120. Some methods for solving linear algebraic systems will be used. Equivalent Course(s): STAT 27725. Equivalent Course(s): CAPP 30350, CMSC 30350. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Students must be admitted to the joint MS program. 100 Units. 100 Units. The Leibniz Institute SAFE is seeking to fill the position of a Research Assistant (m/f/d), 50% Position, salary group E13 TV-H. We are looking for a research assistant for the project "From Machine Learning to Machine Teaching (ML2MT) - Making Machines AND Humans Smarter" funded by Volkswagen Foundation with Prof. Pelizzon being one of . Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. This course is a direct continuation of CMSC 14300. Machine learning algorithms are also used in data modeling. Matlab, Python, Julia, R). Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source . Gaussian mixture models and Expectation Maximization 100 Units. Computer Science offers an introductory sequence for students interested in further study in computer science: Students with no prior experience in computer science should plan to start the sequence at the beginning in CMSC14100 Introduction to Computer Science I. Prerequisite(s): By consent of instructor and approval of department counselor. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. This course covers the basics of computer systems from a programmer's perspective. Prerequisite(s): CMSC 15400. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. In addition, we will discuss advanced topics regarding recent research and trends. The textbooks will be supplemented with additional notes and readings. CMSC23530. Instructor(s): Y. LiTerms Offered: Autumn Computability topics are discussed (e.g., the s-m-n theorem and the recursion theorem, resource-bounded computation). Two exams (20% each). Jointly with the School of the Art Institute of Chicago (SAIC), this course will examine privacy and security issues at the intersection of the physical and digital worlds. Directly from the pages of the book: While machine learning has seen many success stories, and software is readily available to design and train rich and flexible machine learning systems, we believe that the mathematical foundations of machine learning are important in order to understand fundamental principles upon which more complicated machine learning systems are built. The final grade will be allocated to the different components as follows: Homework (50% UG, 40% G): There are roughly weekly homework assignments (about 8 total). Honors Combinatorics. Note(s): This course meets the general education requirement in the mathematical sciences. The minor adviser must approve the student's Consent to Complete a Minor Programform, and the student must submit that form to the student's College adviser by theend of Spring Quarter of the student's third year. Request form available online https://masters.cs.uchicago.edu Equivalent Course(s): MPCS 51250. CMSC25700. This course covers the basics of the theory of finite graphs. CMSC22200. STAT 30900 / CMSC 3781: Mathematical Computation I Matrix Computation, STAT 31015 / CMSC 37811: Mathematical Computation II Convex Optimization, STAT 37710 / CMSC 35400: Machine Learning, TTIC 31150/CMSC 31150: Mathematical Toolkit. This course focuses on the principles and techniques used in the development of networked and distributed software. Least squares, linear independence and orthogonality Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. Roger Lee : Mathematical Foundations of Option Pricing/Numerical methods . Visualizations will be primarily web-based, using D3.js, and possibly other higher-level languages and libraries. All paths prepare students with the toolset they need to apply these skills in academia, industry, nonprofit organizations, and government. CMSC21800. Multimedia Programming as an Interdisciplinary Art I. Note(s): This course meets the general education requirement in the mathematical sciences. Note(s): This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 27200 and 27400. Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. (And how do we ensure this in the presence of failures?) In recent offerings, students have written programs to simulate a model of housing segregation, determine the number of machines needed at a polling place, and analyze tweets from presidential debates. In order to make the operations of the computer more transparent, students will study the C programming language, with special attention devoted to bit-level programming, pointers, allocation, file input and output, and memory layout. Equivalent Course(s): MPCS 54233. Mathematical Logic II. Reading and Research in Computer Science. Instructor(s): Michael MaireTerms Offered: Winter The textbooks will be supplemented with additional notes and readings. Other topics include basic counting, linear recurrences, generating functions, Latin squares, finite projective planes, graph theory, Ramsey theory, coloring graphs and set systems, random variables, independence, expected value, standard deviation, and Chebyshev's and Chernoff's inequalities. Quizzes (10%): Quizzes will be via canvas and cover material from the past few lectures. Note(s): This course meets the general education requirement in the mathematical sciences. CMSC16100. Data science is more than a hot tech buzzword or a fashionable career; in the century to come, it will be an essential toolset in almost any field. Model selection, cross-validation Search 209,580,570 papers from all fields of science. Random forests, bagging 100 Units. Basic counting is a recurring theme. Outline: This course is an introduction to key mathematical concepts at the heart of machine learning. Basic apprehension of calculus and linear algebra is essential. Sensing, actuation, and mediation capabilities of mobile devices are transforming all aspects of computing: uses, networking, interface, form, etc. Instructor(s): Stuart KurtzTerms Offered: TBD Data science provides tools for gaining insight into specific problems using data, through computation, statistics and visualization. The kinds of things you will learn may include mechanical design and machining, computer-aided design, rapid prototyping, circuitry, electrical measurement methods, and other techniques for resolving real-world design problems. What makes an algorithm Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Standard machine learning (ML) approaches often assume that the training and test data follow similar distributions, without taking into account the possibility of adversaries manipulating either distribution or natural distribution shifts. In my opinion, this is the best book on mathematical foundations of machine learnign there is. Lang and Roxie: Tuesdays 12:30 pm to 1:30pm, Crerar 298 (there will be slight changes for 2nd week and 4th week, i.e., Oct. 8th and Oct. 22 due to the reservation problem, and will be updated on Canvas accordingly), Tayo: Mondays 11am-12pm in Jones 304 (This session is NOT for homework help, but rather for additional help with lectures and fundamentals. Students who major in computer science have the option to complete one specialization. Practical exercises in writing language transformers reinforce the the theory. Introduction to Computer Security. 100 Units. Equivalent Course(s): DATA 11800, STAT 11800. Students are expected to have taken calculus and have exposure to numerical computing (e.g. The only opportunity students will have to complete the retired introductory sequence is as follows: Students who are not able to complete the retired introductory sequence on this schedule should contact the Director of Undergraduate Studies for Computer Science or the Computer Science Major Adviser for guidance. 100 Units. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100. We will build and explore a range of models in areas such as infectious disease and drug resistance, cancer diagnosis and treatment, drug design, genomics analysis, patient outcome prediction, medical records interpretation and medical imaging. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. We are expanding upon the conventional view of data sciencea combination of statistics, computer science and domain expertiseto build out the foundations of the field, consider its ethical and societal implications and communicate its discoveries to make the most powerful and positive real-world impact.. This course will not be offered again. Topics include automata theory, regular languages, context-free languages, and Turing machines. This course deals with numerical linear algebra, approximation of functions, approximate integration and differentiation, Fourier transformation, solution of nonlinear equations, and the approximate solution of initial value problems for ordinary differential equations. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. Placement into MATH 15100 or completion of MATH 13100. Mathematical Foundations of Machine Learning. While a student may enroll in CMSC 29700 or CMSC 29900 for multiple quarters, only one instance of each may be counted toward the major. Quizzes: 30%. This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. ); internet and routing protocols (IP, IPv6, ARP, etc. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. C+: 77% or higher When dealing with under-served and marginalized communities, achieving these goals requires us to think through how different constraints such as costs, access to resources, and various cognitive and physical capabilities shape what socio-technical systems can best address a particular issue. Prerequisite(s): CMSC 15400 and (CMSC 27100 or CMSC 27130 or CMSC 37110). Homework problems include both mathematical derivations and proofs as well as more applied problems that involve writing code and working with real or synthetic data sets. At what level does an entering student begin studying computer science at the University of Chicago? This course is an introduction to "big" data engineering where students will receive hands-on experience building and deploying realistic data-intensive systems. The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. Linear algebra strongly recommended; a 200-level Statistics course recommended. Instructor(s): G. KindlmannTerms Offered: Winter The centerpiece will be the new Data Science Clinic, a capstone, two-quarter sequence that places students on teams with public interest organizations, government agencies, industrial partners, and researchers. provides a systematic view of a range of machine learning algorithms, Appropriate for undergraduate students who have taken. B: 83% or higher Systems Programming I. The book is available at published by Cambridge University Press (published April 2020). Parallel Computing. The objective is that everyone creates their own, custom-made, functional I/O device. Cryptography is the use of algorithms to protect information from adversaries. Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. Rob Mitchum. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Topics covered include two parts: (1) a gentle introduction of machine learning: generalization and model selection, regression and classification, kernels, neural networks, clustering and dimensionality reduction; (2) a statistical perspective of machine learning, where we will dive into several probabilistic supervised and unsupervised models, including logistic regression, Gaussian mixture models, and generative adversarial networks. Programming languages often conflate the definition of mathematical functions, which deterministically map inputs to outputs, and computations that effect changes, such as interacting with users and their machines. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. Simple type theory, strong normalization. But the Introduction to Data Science sequence changed her view. Note(s): This course is offered in alternate years. CMSC23200. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Features and models The combination of world-class liberal arts education, sophisticated theoretical examination, and exploration of relevant, real-world problems as integral to the major is invaluable for graduates to establish a rewarding career. CMSC14400. We compliment the lectures with weekly programming assignments and two larger projects, in which we build/program/test user-facing interactive systems. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Prerequisite(s): A year of calculus (MATH 15300 or higher), a quarter of linear algebra (MATH 19620 or higher), and CMSC 10600 or higher; or consent of instructor. Instructor(s): Allyson EttingerTerms Offered: Autumn Introduction to Computer Systems. Where do breakthrough discoveries and ideas come from? Although this course is designed to be at the level of mathematical sciences courses in the Core, with little background required, we expect the students to develop computational skills that will allow them to analyze data. The Core introduces students to a world of general knowledge useful for the active, but highly thoughtful practice of modern citizenship, while our brilliant majors enable students to gain active experience in the excitement of fundamental, pathbreaking research. CMSC 25025-1: Machine Learning and Large-Scale Data Analysis (Amit) CMSC 25300-1: Mathematical Foundations of Machine Learning (Jonas) CMSC 25910-1: Engineering for Ethics, Privacy, and Fairness in Computer Systems (Ur) CMSC 27200-1: Theory of Algorithms (Orecchia) [Theory B] CMSC 27200-2: Theory of Algorithms (Orecchia) [Theory B] Machine Learning and Large-Scale Data Analysis. Plan accordingly. The course will consist of bi-weekly programming assignments, a midterm examination, and a final. This course is cross-listed between CS, ECE, and . Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss The course will demonstrate how computer systems can violate individuals' privacy and agency, impact sub-populations in disparate ways, and harm both society and the environment. Hands-On experience building and deploying realistic data-intensive systems are not allowed to register for CMSC 22300 introductory of. 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