The Center for Data and Computing is an intellectual hub and incubator for data science and artificial intelligence research at the University of Chicago. Prerequisite(s): CMSC 15400. 1. Model selection, cross-validation Probabilistic Machine Learning: An Introduction; by Kevin Patrick Murphy, MIT Press, 2021. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation and communication of results. mathematical foundations of machine learning uchicago. Features and models Vectors and matrices in machine learning models Massive Open Online Courses (MOOCs) were created to bring education to those without access to universities, yet most of the students who succeed in them are those who are already successful in the current educational model. Data science provides tools for gaining insight into specific problems using data, through computation, statistics and visualization. Students will gain experience applying neural networks to modern problems in computer vision, natural language processing, and reinforcement learning. The Data Science Clinic will provide an understanding of the life cycle of a real-world data science project, from inception and gathering, to modeling and iteration to engineering and implementation, said David Uminsky, executive director of the UChicago Data Science Initiative. The course will combine analysis and discussion of these approaches with training in the programming and mathematical foundations necessary to put these methods into practice. This course deals with finite element and finite difference methods for second-order elliptic equations (diffusion) and the associated parabolic and hyperbolic equations. This course includes a project where students will have to formulate hypotheses about a large dataset, develop statistical models to test those hypotheses, implement a prototype that performs an initial exploration of the data, and a final system to process the entire dataset. 100 Units. Designed to provide an understanding of the key scientific ideas that underpin the extraordinary capabilities of today's computers, including speed (gigahertz), illusion of sequential order (relativity), dynamic locality (warping space), parallelism, keeping it cheap - and low-energy (e-field scaling), and of course their ability as universal information processing engines. Computer science majors must take courses in the major for quality grades. Students will continue to use Python, and will also learn C and distributed computing tools and platforms, including Amazon AWS and Hadoop. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. Simple type theory, strong normalization. Dependent types. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. Engineering for Ethics, Privacy, and Fairness in Computer Systems. MIT Press, Second Edition, 2018. Foundations Courses - 250 units. Equivalent Course(s): MPCS 54233. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. Autumn/Spring. Programming projects will be in C and C++. CMSC27410. As such it has been a fertile ground for new statistical and algorithmic developments. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. This is a graduate-level CS course with the main target audience being TTIC PhD students (for which it is required) and other CS, statistics, CAM and math PhD students with an interest in machine learning. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. It also touches on some of the legal, policy, and ethical issues surrounding computer security in areas such as privacy, surveillance, and the disclosure of security vulnerabilities. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200, and the equivalent of two quarters of calculus (MATH 13200 or higher). Students do reading and research in an area of computer science under the guidance of a faculty member. Equivalent Course(s): MAAD 25300. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110, or by consent. Prerequisite(s): CMSC 15400 The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. CMSC16100. 100 Units. Rather than emailing questions to the teaching staff, we encourage you to post your questions on Ed Discussion. Both the BA and BS in computer science require fulfillment of the general education requirement in the mathematical sciences by completing an approved two-quarter calculus sequence. CMSC12100-12200-12300. This course will examine how to design for security and privacy from a user-centered perspective by combining insights from computer systems, human-computer interaction (HCI), and public policy. Introduction to Complexity Theory. Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. Linear classifiers Part 1 covered by Mathematics for Machine Learning). Prerequisite(s): CMSC 15400 Non-MPCS students must receive approval from program prior to registering. This course aims to introduce computer scientists to the field of bioinformatics. The textbooks will be supplemented with additional notes and readings. Methods of enumeration, construction, and proof of existence of discrete structures are discussed in conjunction with the basic concepts of probability theory over a finite sample space. Verification techniques to evaluate the correctness of quantum software and hardware will also be explored. This course provides an introduction to the concepts of parallel programming, with an emphasis on programming multicore processors. Quizzes: 30%. Note(s): Prerequisites: CMSC 15400 or equivalent, or graduate student. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. Equivalent Course(s): MAAD 23220. Foundations of Machine Learning. "The urgency with which businesses need strong data science talent is rapidly increasing, said Kjersten Moody, AB98 and chief data officer at Prudential Financial. 100 Units. The course will provide an introduction to quantum computation and quantum technologies, as well as classical and quantum compiler techniques to optimize computations for technologies. See also some notes on basic matrix-vector manipulations. 100 Units. 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. A-: 90% or higher TTIC 31180: Probabilistic Graphical Models (Walter) Spring. 100 Units. CMSC11800. Search 209,580,570 papers from all fields of science. This course provides an introduction to basic Operating System principles and concepts that form as fundamental building blocks for many modern systems from personal devices to Internet-scale services. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. His group developed mathematical models based on this data and then began using machine-learning methods to reveal new information about proteins' basic design rules. Computation will be done using Python and Jupyter Notebook. It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 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. We also study some prominent applications of modern computer vision such as face recognition and object and scene classification. Terms Offered: Alternate years. . The use of physical robots and real-world environments is essential in order for students to 1) see the result of their programs 'come to life' in a physical environment and 2) gain experience facing and overcoming the challenges of programming robots (e.g., sensor noise, edge cases due to environment variability, physical constraints of the robot and environment). Mathematics (1) Mechanical Engineering (1) Photography (1) . Mathematical Foundations of Machine Learning. Through both computer science and studio art, students will design algorithms, implement systems, and create interactive artworks that communicate, provoke, and reframe pervasive issues in modern privacy and security. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, the concepts of polynomial-time algorithms, and NP-completeness. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. In the context of the C language, the course will revisit fundamental data structures by way of programming exercises, including strings, arrays, lists, trees, and dictionaries. 100 Units. The course revolves around core ideas behind the management and computation of large volumes of data ("Big Data"). Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 Tensions often arise between a computer system's utility and its privacy-invasiveness, between its robustness and its flexibility, and between its ability to leverage existing data and existing data's tendency to encode biases. We will write code in JavaScript and related languages, and we will work with a variety of digital media, including vector graphics, raster images, animations, and web applications. It describes several important modern algorithms, provides the theoretical . Programming will be based on Python and R, but previous exposure to these languages is not assumed. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security Matlab, Python, Julia, or R). Introduction to Human-Computer Interaction. We concentrate on a few widely used methods in each area covered. CMSC23240. CMSC15100-15200. This course is the second quarter of a two-quarter systematic introduction to the foundations of data science, as well as to practical considerations in data analysis. Instructor(s): G. KindlmannTerms Offered: Spring The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), a multi-institutional collaboration of Chicago universities studying the foundations and applications of data science, was expanded and renewed for five years through a $10 million grant from the National Science Foundation. STAT 34000: Gaussian Processes (Stein) Spring. Building upon the data science minor and the Introduction to Data Science sequence taught by Franklin and Dan Nicolae, professor and chair in the Department of Statistics and the College, the major will include new courses and emphasize research and application. Rather than emailing questions to the teaching staff, we encourage you to post your questions on, We will not be accepting auditors this quarte. The PDF will include all information unique to this page. Introduction to Computer Security. 100 Units. Model selection, cross-validation - Financial Math at UChicago literally . AI & Machine Learning Foundations and applications of computer algorithms making data-centric models, predictions, and decisions Modern machine learning techniques have ushered in a new era of computing. Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 Both BA and BS students take at least fourteen computer science courses chosen from an approved program. High-throughput automated biological experiments require advanced algorithms, implemented in high-performance computing systems, to interpret their results. CMSC25460. Computability topics are discussed (e.g., the s-m-n theorem and the recursion theorem, resource-bounded computation). 30546. Application: text classification, AdaBoost Introduction to Neural Networks. This course introduces the fundamental concepts and techniques in data mining, machine learning, and statistical modeling, and the practical know-how to apply them to real-world data through Python-based software. 100 Units. 100 Units. Instructor(s): H. GunawiTerms Offered: Autumn The topics covered in this course will include software, data mining, high-performance computing, mathematical models and other areas of computer science that play an important role in bioinformatics. Instructor(s): Stuart KurtzTerms Offered: TBD The rst half of the book develops Boolean type theory | a type-theoretic formal foundation for mathematics designed speci cally for this course. 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. Honors Graph Theory. Students must be admitted to the joint MS program. CMSC15100. Introduction to Optimization. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. Equivalent Course(s): CMSC 30280, MAAD 20380. CMSC25900. Topics include number theory, Peano arithmetic, Turing compatibility, unsolvable problems, Gdel's incompleteness theorem, undecidable theories (e.g., the theory of groups), quantifier elimination, and decidable theories (e.g., the theory of algebraically closed fields). While this course is not a survey of different programming languages, we do examine the design decisions embodied by various popular languages in light of their underlying formal systems. Quizzes (10%): Quizzes will be via canvas and cover material from the past few lectures. Live class participation is not mandatory, but highly encourage (there will be no credit penalty for not participating in the live sessions, but students are expected to do so to get the best from the course). Exams: 40%. Team projects are assessed based on correctness, elegance, and quality of documentation. They will also wrestle with fundamental questions about who bears responsibility for a system's shortcomings, how to balance different stakeholders' goals, and what societal values computer systems should embed. 100 Units. Topics include shortest paths, spanning trees, counting techniques, matchings, Hamiltonian cycles, chromatic number, extremal graph theory, Turan's theorem, planarity, Menger's theorem, the max-flow/min-cut theorem, Ramsey theory, directed graphs, strongly connected components, directed acyclic graphs, and tournaments. Instructor(s): Sarah SeboTerms Offered: Winter CMSC28000. We cover various standard data structures, both abstractly, and in terms of concrete implementations-primarily in C, but also from time to time in other contexts like scheme and ksh. Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. No matter where I go after graduation, I can help make sense of chaos in whatever kind of environment I'm working in.. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. Certificate Program. 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. : Winter CMSC28000 emailing questions to the concepts of polynomial-time algorithms, and Ameet.! Learning: an Introduction to the joint MS program mathematical foundations of machine learning uchicago AdaBoost Introduction to networks. To introduce computer scientists to the field of bioinformatics based on correctness, elegance, and NP-completeness surveillance Aesthetics Provocations... 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