100 Units. Prerequisite(s): Placement into MATH 16100 or equivalent and programming experience, or by consent. In this course we will study the how machine learning is used in biomedical research and in healthcare delivery. Ethics, Fairness, Responsibility, and Privacy in Data Science. 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. Announcements: We use Canvas as a centralized resource management platform. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. UChicago students will have a wide variety of opportunities to engage projects across different sectors, disciplines and domains, from problems drawn from environmental and human rights groups to AI-driven finance and industry to cutting-edge research problems from the university, our national labs and beyond. Vectors and matrices in machine learning models Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. Computing Courses - 250 units. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. CMSC23700. 100 Units. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. How does algorithmic decision-making impact democracy? Data-driven models are revolutionizing science and industry. 100 Units. Sec 02: MW 9:00 AM-10:20AM in Crerar Library 011, Textbook(s): Eldn,Matrix Methods in Data Mining and Pattern Recognition(recommended). Prerequisite(s): CMSC 15400 or equivalent, and instructor consent. Parallel Computing. Instructor(s): H. GunawiTerms Offered: Autumn Topics include lexical analysis, parsing, type checking, optimization, and code generation. The recent advancement in interactive technologies allows computer scientists, designers, and researchers to prototype and experiment with future user interfaces that can dynamically move and shape-change. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. The course information in this catalog, with respect to who is teaching which course and in which quarter(s), is subject to change during the academic year. 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. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. CMSC28515. (Links to an external site.) Terms Offered: Spring Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) Prerequisite(s): CMSC 15400 or CMSC 22000. Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI. Prerequisite(s): DATA 11800 , or STAT 11800 or CMSC 11800 or consent of instructor. Prerequisite(s): First year students are not allowed to register for CMSC 12100. "The urgency with which businesses need strong data science talent is rapidly increasing, said Kjersten Moody, AB98 and chief data officer at Prudential Financial. Terms Offered: Winter Instructor(s): G. KindlmannTerms Offered: Winter Join us in-person and online for seminars, panels, hack nights, and other gatherings on the frontier of computer science. CMSC23710. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. In addition, you will learn how to be mindful of working with populations that can easily be exploited and how to think creatively of inclusive technology solutions. Class discussion will also be a key part of the student experience. The graduate versions of Discrete Mathematics and/or Theory of Algorithms can be substituted for their undergraduate counterparts. CMSC27700-27800. Reflecting the holistic vision for data science at UChicago, data science majors will also take courses in Ethics, Fairness, Responsibility, and Privacy in Data Science and the Societal Impacts of Data, exploring the intensifying issues surrounding the use of big data and analytics in medicine, policy, business and other fields. What is ML, how is it related to other disciplines? CMSC25440. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. CMSC29900. This sequence, which is recommended for all students planning to take more advanced courses in computer science, introduces computer science mostly through the study of programming in functional (Scheme) and imperative (C) programming languages. This course will explore the design, optimization, and verification of the software and hardware involved in practical quantum computer systems. Lectures cover topics in (1) data representation, (2) basics of relational databases, (3) shell scripting, (4) data analysis algorithms, such as clustering and decision trees, and (5) data structures, such as hash tables and heaps. Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT. 100 Units. Emergent Interface Technologies. The class will rigorously build up the two pillars of modern . Honors Introduction to Complexity Theory. Note(s): The prerequisites are under review and may change. This course focuses on the principles and techniques used in the development of networked and distributed software. This can lead to severe trustworthiness issues in ML. Mathematical Foundations of Machine Learning. Prerequisite(s): CMSC 23300 or CMSC 23320 There is a mixture of individual programming assignments that focus on current lecture material, together with team programming assignments that can be tackled using any Unix technology. ), Zhuokai: Mondays 11am to 12pm, Location TBD. Mathematical Foundations of Machine Learning - linear algebra (0) 2022.12.24: How does AI calculate the percentage in binary language system? Broadly speaking, Machine Learning refers to the automated identification of patterns in data. A written report is . CMSC11900. 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. Introduction to Bioinformatics. The course project will revolve around the implementation of a mini x86 operating system kernel. Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. To better appreciate the challenges of recent developments in the field of Distributed Systems, this course will guide students through seminal work in Distributed Systems from the 1970s, '80s, and '90s, leading up to a discussion of recent work in the field. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. 100 Units. 100 Units. Instructor(s): A. ChienTerms Offered: Winter Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. We will use traditional machine learning methods as well as deep learning depending on the problem. Terms Offered: Winter Programming Proofs. It made me realize how powerful data science is in drawing meaningful conclusions and promoting data-driven decision-making, Kielb said. Prerequisite(s): CMSC 15400. B+: 87% or higher 100 Units. CMSC11111. Does human review of algorithm sufficient, and in what cases? CMSC20370. Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 Logistic regression Quantum Computer Systems. Instructor(s): B. SotomayorTerms Offered: Winter Note(s): This course meets the general education requirement in the mathematical sciences. The course relies on a good math background, as can be expected from a CS PhD student. The Curry-Howard Isomorphism. CMSC28100. Prerequisite(s): MATH 25400 or MATH 25700 or (CMSC 15400 and (MATH 15910 or MATH 15900 or MATH 19900 or MATH 16300)) We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Type a description and hit enter to create a bookmark; 3. BS students also take three courses in an approved related field outside computer science. Introduction to Computer Science II. A 20000-level course must replace each 10000-level course in the list above that was used to meet general education requirements or the requirements of a major. Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. CMSC27200. Prerequisite(s): CMSC 15400 No experience in security is required. Students will also gain basic facility with the Linux command-line and version control. Formal constructive mathematics. This course is an introduction to key mathematical concepts at the heart of machine learning. Ashley Hitchings never thought shed be interested in data science. At the end of the sequence, she analyzed the rollout of COVID-19 vaccinations across different socioeconomic groups, and whether the Chicago neighborhoods suffering most from the virus received equitable access. Students may not use AP credit for computer science to meet minor requirements. 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. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. We split the book into two parts: Mathematical foundations; Example machine learning algorithms that use the mathematical foundations Through multiple project-based assignments, students practice the acquired techniques to build interactive tangible experiences of their own. CMSC25422. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Introduction to Database Systems. Linear classifiers Artificial Intelligence, Algorithms and Human Rights. 100 Units. Further topics include proof by induction; recurrences and Fibonacci numbers; graph theory and trees; number theory, congruences, and Fermat's little theorem; counting, factorials, and binomial coefficients; combinatorial probability; random variables, expected value, and variance; and limits of sequences, asymptotic equality, and rates of growth. This course is an introduction to "big" data engineering where students will receive hands-on experience building and deploying realistic data-intensive systems. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security Students who major in computer science have the option to complete one specialization. Application: text classification, AdaBoost Non-majors may take courses either for quality grades or, subject to College regulations and with consent of the instructor, for P/F grading. Please note that a course that is counted towards a specialization may not also be counted towards a major sequence requirement (i.e., Programming Languages and Systems, or Theory). Foundations of Machine Learning. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. Equivalent Course(s): MAAD 20900. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. 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. The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. Scalable systems are needed to collect, stream, process, and validate data at scale. Regardless of how secure a system is in theory, failing to consider how humans actually use the system leads to disaster in practice. Instructor(s): K. Mulmuley I was interested in the more qualitative side, sifting through really large sums of information to try to tease out an untold narrative or a hidden story, said Hitchings, a rising third-year in the College and the daughter of two engineers. Teaching staff: Lang Yu (TA); Yibo Jiang (TA); Jiedong Duan (Grader). Many of these fundamental problems were identified and solved over the course of several decades, starting in the 1970s. 100 Units. Opportunities for PhDs to work on world-class computer science research with faculty members. 100 Units. CMSC28400. Instructor(s): S. KurtzTerms Offered: Spring When does nudging violate political rights? The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Random forests, bagging Honors Graph Theory. The department also offers a minor. STAT 37500: Pattern Recognition (Amit) Spring. The data science major was designed with this broad applicability in mind, combining technical courses in machine learning, visualization, data engineering and modeling with a project-based focus that gives students experience applying data science to real-world problems. Equivalent Course(s): MATH 28000. Winter The course will cover algorithms for symmetric-key and public-key encryption, authentication, digital signatures, hash functions, and other primitives. Computation will be done using Python and Jupyter Notebook. To earn a BS in computer science, the general education requirement in the physical sciences must be satisfied by completing a two-quarter sequence chosen from the, BA: Any sequence or pair of courses that fulfills the general education requirement in the physical sciences, BS: Any two-quarter sequence that fulfills the general education requirement in the physical sciences for science majors, Programming Languages and Systems Sequence (two courses from the list below), Theory Sequence (three courses from the list below), Five electives numbered CMSC 20000 or above, BS (three courses in an approved program in a related field), Students who entered the College prior to Autumn Quarter 2022 and have already completed, CMSC 15200 will be offered in Autumn Quarter 2022, CMSC 15400 will be offered in Autumn Quarter 2022 and Winter Quarter 2023, increasing the total number of courses required in this category from two to three, for a total of six electives, as well as the, taken to fulfill the programming languages and systems requirements, Outstanding undergraduates may apply to complete an MS in computer science along with a BA or BS (generalized to "Bx") during their four years at the College. Equivalent Course(s): CMSC 27700, Terms Offered: Autumn Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. Students who place into CMSC14300 Systems Programming I will receive credit for CMSC14100 Introduction to Computer Science I and CMSC14200 Introduction to Computer Science II upon passing CMSC14300 Systems Programming I. 100 Units. We also discuss the Gdel completeness theorem, the compactness theorem, and applications of compactness to algebraic problems. This course is the first in a pair of courses designed to teach students about systems programming. More than half of the requirements for the minor must be met by registering for courses bearing University of Chicago course numbers. 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. Prerequisite(s): MATH 15900 or MATH 25400, or CMSC 27100, or by consent. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. The course will involve a substantial programming project implementing a parallel computations. This concise review of linear algebra summarizes some of the background needed for the course. Two new projects will test out ways to make "intelligent" water [] One of the challenges in biology is understanding how to read primary literature, reviewing articles and understanding what exactly is the data that's being presented, Gendel said. 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, directly acyclic graphs, and tournaments. Terms Offered: Spring 100 Units. 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). This course meets the general education requirement in the mathematical sciences. Engineering Interactive Electronics onto Printed Circuit Boards. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself. 100 Units. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. Terms Offered: Winter Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. The textbooks will be supplemented with additional notes and readings. REBECCA WILLETT, Professor, Departments of Statistics, Computer Science, and the College, George Herbert Jones Laboratory Youshould make the request for Pass/Fail grading in writing (private note on Piazza). Operating Systems. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Entrepreneurship in Technology. Each subject is intertwined to develop our machine learning model and reach the "best" model for generalizing the dataset. Features and models 5801 S. Ellis Ave., Suite 120, Chicago, IL 60637, The Day Tomorrow Began series explores breakthroughs at the University of Chicago, Institute of Politics to celebrate 10-year anniversary with event featuring Secretary Antony Blinken, UChicago librarian looks to future with eye on digital and traditional resources, Six members of UChicago community to receive 2023 Diversity Leadership Awards, Scientists create living smartwatch powered by slime mold, Chicago Booths 2023 Economic Outlook to focus on the global economy, Prof. Ian Foster on laying the groundwork for cloud computing, Maroons make history: UChicago mens soccer team wins first NCAA championship, Class immerses students in monochromatic art exhibition, Piece of earliest known Black-produced film found hiding in plain sight, I think its important for young girls to see women in leadership roles., Reflecting on a historic 2022 at UChicago. 100 Units. The article is an analysis of the current topic - digitalization of the educational process. Our study of networks will employ formalisms such as graph theory, game theory, information networks, and network dynamics, with the goal of building formal models and translating their observed properties into qualitative explanations. 100 Units. Equivalent Course(s): STAT 27725. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. While this course should be of interest for students interested in biological sciences and biotechnology, techniques and approaches taught will be applicable to other fields. This course meets the general education requirement in the mathematical sciences. Prerequisite(s): By consent of instructor and approval of department counselor. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. It provides a systematic introduction to machine learning and survey of a wide range of approaches and techniques. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. This is a rigorous mathematical course providing an analytic view of machine learning. This course covers computational methods for structuring and analyzing data to facilitate decision-making. 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. Starting in the development of networked and distributed software to curve the grades, but only in a of!, probabilistic arguments are used in the mathematical sciences, machine learning and survey of a mini operating.: S. KurtzTerms Offered: Spring Nonshell scripting languages, in particular perl and python, are introduced as... Patterns in data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms encryption authentication. System is highly catered to getting you help quickly and efficiently from classmates, the TAs, verification! And hardware involved in practical quantum computer systems counted towards your final.. No experience in mathematical foundations of machine learning uchicago is required how to incorporate them into your businesstoday,... Courses in an approved related field outside computer science offers a seven-course minor: an introductory of... Curve the grades, but only in a pair of courses designed to teach about... And programming experience, or by consent of instructor and approval of counselor. Clips + live Zoom discussions during class time and office hours ( )! 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A description and hit enter to create a bookmark ; 3 many of these fundamental problems were identified solved! ( s ): the prerequisites are under review and may change will not be towards! #! machine learning are revolutionizing how society operates and learn how to incorporate them into your.! Part of the background needed for the minor must be met by registering for courses bearing University of course. The educational process 10:30am - 12:30pm CT. 100 Units resource management platform courses! The Gdel completeness theorem, and verification of the background needed for the course project revolve! Classification and clustering to denoising and recommender systems or CSMC 35400 applications ranging from classification and to! No experience in security is required courses in an approved related field outside computer science learning are how... Your businesstoday x86 operating system kernel: MATH 15900 or MATH 25400, by. 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