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. Creating technologies that are inclusive of people in marginalized communities involves more than having technically sophisticated algorithms, systems, and infrastructure. relationship between worldmaking and technology through social, political, and technical lenses. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. 2017 The University of Chicago This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. Introduction to Cryptography. 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. Equivalent Course(s): CMSC 30600. Introduction to Computer Science I. 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. Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) Note(s): The prerequisites are under review and may change. This is not a book about foundations in the sense that this is where you should start if you want to learn about machine learning. The Barendregt cube of type theories. Inventing, Engineering and Understanding Interactive Devices. Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT. Equivalent Course(s): CMSC 27700, Terms Offered: Autumn Practical exercises in writing language transformers reinforce the the theory. Students will partner with organizations on and beyond campus to advance research, industry projects and social impact through what they have learned, transcending the conventional classroom experience., The Colleges new data science major offers students a remarkable new interdisciplinary learning opportunity, said John W. Boyer, dean of the College. Pattern Recognition and Machine Learning by Christopher Bishop(Links to an external site.) CMSC25460. 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. Linear algebra strongly recommended; a 200-level Statistics course recommended. The course culminates in the production and presentation of a capstone interactive artwork by teams of computer scientists and artists; successful products may be considered for prototyping at the MSI. 100 Units. What is ML, how is it related to other disciplines? 5801 S. 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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. Prerequisite(s): CMSC 23300 with at least a B+, or by consent. Mathematical Foundations of Machine Learning. 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. Prerequisite(s): CMSC 15400 and (CMSC 27100 or CMSC 27130 or CMSC 37110). Courses that fall into this category will be marked as such. Is algorithmic bias avoidable? What is ML, how is it related to other disciplines? Equivalent Course(s): MPCS 51250. Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. Students should consult course-info.cs.uchicago.edufor up-to-date information. Note Inclusive Technology: Designing for Underserved and Marginalized Populations. The iterative nature of the design process will require an appreciable amount of time outside of class for completing projects. Instructor(s): H. GunawiTerms Offered: Autumn 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 is a project oriented course in which students will construct a fully working compiler, using Standard ML as the implementation language. Note(s): This course meets the general education requirement in the mathematical sciences. - "Online learning: theory, algorithms and applications ( . CMSC25900. Bookmarks will appear here. Advanced Networks. Students who place out of CMSC14400 Systems Programming II based on the Systems Programming Exam are required to take an additional computer science elective course for a total of six electives, as well as the additional Programming Languages and Systems Sequence course mentioned above. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Vectors and matrices in machine learning models Instructor(s): A. RazborovTerms Offered: Autumn The major requires five additional elective computer science courses numbered 20000 or above. Data-driven models are revolutionizing science and industry. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. 100 Units. CMSC21800. Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. A major goal of this course is to enable students to formalize and evaluate theoretical claims. Prerequisite(s): First year students are not allowed to register for CMSC 12100. 100 Units. Programming Proofs. Equivalent Course(s): STAT 11900, DATA 11900. Further topics include proof by induction; number theory, congruences, and Fermat's little theorem; relations; factorials, binomial coefficients and advanced counting; combinatorial probability; random variables, expected value, and variance; graph theory and trees. Scientific visualization combines computer graphics, numerical methods, and mathematical models of the physical world to create a visual framework for understanding and solving scientific problems. Courses fulfilling general education requirements must be taken for quality grades. Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. Outline: This course is an introduction to key mathematical concepts at the heart of machine learning. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. Instructor(s): K. Mulmuley This class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer primarily to tabulated data). Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. Quizzes: 30%. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 Prerequisite(s): CMSC 11900 or 12200 or CMSC 15200 or CMSC 16200. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Many of these fundamental problems were identified and solved over the course of several decades, starting in the 1970s. You will learn about different underserved and marginalized communities such as children, the elderly, those needing assistive technology, and users in developing countries, and their particular needs. This course is an introduction to key mathematical concepts at the heart of machine learning. Instructor(s): B. SotomayorTerms Offered: Spring Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss Students may enroll in CMSC29700 Reading and Research in Computer Science and CMSC29900 Bachelor's Thesis for multiple quarters, but only one of each may be counted as a major elective. Search . Equivalent Course(s): LING 21010, LING 31010, CMSC 31010. 100 Units. CMSC23310. During Foundations Year, students also take a number of Content and Methods Courses in literacy, math, science, and social science to fulfill requirements for both the elementary and middle grades endorsement pathways. (A full-quarter course is 100 units, with courses that take place in the first-half or second-half of the quarter being 50 units.) The course revolves around core ideas behind the management and computation of large volumes of data ("Big Data"). The class provides a range of basic engineering techniques to allow students to develop their own actuated user interface systems, including 3D mechanical design, digital fabrication (e.g. Prerequisite(s): CMSC 15400 The computer science program offers BA and BS degrees, as well as combined BA/MS and BS/MS degrees. It provides a systematic introduction to machine learning and survey of a wide range of approaches and techniques. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200. At what level does an entering student begin studying computer science at the University of Chicago? 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 could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. In this course, we will explore the use of proof assistants, computer programs that allow us to write, automate, and mechanically check proofs. Now supporting the University of Chicago. Honors Combinatorics. ); end-to-end protocols (UDP, TCP); and other commonly used network protocols and techniques. Join us in-person and online for seminars, panels, hack nights, and other gatherings on the frontier of computer science. Machine Learning and Large-Scale Data Analysis. Instructor(s): Lorenzo OrecchiaTerms Offered: Spring Part 1 covered by Mathematics for Machine Learning). A core theme of the course is "scale," and we will discuss the theory and the practice of programming with large external datasets that cannot fit in main memory on a single machine. 3. This course is an introduction to database design and implementation. The course project will revolve around the implementation of a mini x86 operating system kernel. B: 83% or higher Computer Architecture for Scientists. Prerequisite(s): CMSC 15400. Through the new Data Science Clinic, students will capstone their studies by working with government, non-profit and industry partners on projects using data science approaches in real world situations with immediate, substantial impact. Note(s): Students who have taken CMSC 15100 may take 16200 with consent of instructor. 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. CMSC13600. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. Introduction to Human-Computer Interaction. It will cover the basics of training neural networks, including backpropagation, stochastic gradient descent, regularization, and data augmentation. Other new courses in development will cover misinterpretation of data, the economic value of data and the mathematical foundations of machine learning and data science. Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. Dependent types. CMSC15100. This course introduces mathematical logic. Application: text classification, AdaBoost CMSC20380. Gaussian mixture models and Expectation Maximization 100 Units. Artificial Intelligence, Algorithms and Human Rights. Generally offered alternate years. Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Equivalent Course(s): MAAD 25300. Computer Science with Applications II. Since it was introduced in 2019, the data science minor has drawn interest from UChicago students across disciplines. In the course of collecting and interpreting the known data, the authors cite the pedagogical foundations of digital literacy, the current state of digital learning and problems, and the prospects for the development of this direction in the future are also considered. Each of these mini projects will involve students programming real, physical robots interacting with the real world. 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 made me realize how powerful data science is in drawing meaningful conclusions and promoting data-driven decision-making, Kielb said. 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. This policy allows you to miss class during a quiz or miss an assignment, but only one each. Vectors and matrices in machine learning models Introduction to Computer Graphics. A-: 90% or higher This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Terms Offered: Winter Equivalent Course(s): MATH 28530. There are three different paths to a Bx/MS: a research-oriented program for computer science majors, a professionally oriented program for computer science majors, and a professionally oriented program for non-majors. Weekly problem sets will include both theoretical problems and programming tasks. The mathematical and algorithmic foundations of scientific visualization (for example, scalar, vector, and tensor fields) will be explained in the context of real-world data from scientific and biomedical domains. A computer graphics collective at UChicago pursuing innovation at the intersection of 3D and Deep Learning. 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. 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. 100 Units. I had always viewed data science as something very much oriented toward people passionate about STEM, but the data science sequence really framed it as a tool that anyone in any discipline could employ, to tell stories using data and uncover insights in a more quantitative and rigorous way.. CMSC23220. Note(s): This course meets the general education requirement in the mathematical sciences. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, Pattern Recognition and Machine Learning by Christopher Bishop, Mondays and Wednesdays, 9-10:20am in Crerar 011, Mondays and Wednesdays, 3-4:15pm in Ryerson 251. Computing systems have advanced rapidly and transformed every aspect of our lives for the last few decades, and innovations in computer architecture is a key enabler. CMSC23300. Mathematical Foundations of Machine Learning. Get more with UChicago News delivered to your inbox. 100 Units. Neural networks and backpropagation, Density estimation and maximum likelihood estimation The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. 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 could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. Mathematics (1) Mechanical Engineering (1) Photography (1) . 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. 5747 South Ellis Avenue Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. In this class we will engineer electronics onto Printed Circuit Boards (PCBs). Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. Topics include: Processes and threads, shared memory, message passing, direct-memory access (DMA), hardware mechanisms for parallel computing, synchronization and communication, patterns of parallel programming. Recent approaches have unlocked new capabilities across an expanse of applications, including computer graphics, computer vision, natural language processing, recommendation engines, speech recognition, and models for understanding complex biological, physical, and computational systems. Microsoft. Waitlist: We will not be accepting auditors this quarter due to high demand. Terms Offered: Winter Students are required to complete both written assignments and programming projects using OpenGL. STAT 41500-41600: High Dimensional Statistics. Email policy: The TAs and I will prioritize answering questions posted to Piazza, NOT individual emails. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. We concentrate on a few widely used methods in each area covered. Instructor(s): G. KindlmannTerms Offered: Winter 100 Units. 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. For up-to-date information on our course offerings, please consult course-info.cs.uchicago.edu. The course will involve a business plan, case-studies, and supplemental reading to provide students with significant insights into the resolve required to take an idea to market. Instructor(s): Laszlo BabaiTerms Offered: Spring Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. 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 15400 and one of the following: CMSC 22200, CMSC 22240, CMSC 23000, CMSC 23300, CMSC 23320; or by consent. CMSC22400. 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. Note(s): This course meets the general education requirement in the mathematical sciences. 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. Church's -calculus, -reduction, the Church-Rosser theorem. CMSC22880. Students will gain further fluency with debugging tools and build systems. 100 Units. Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. We also study some prominent applications of modern computer vision such as face recognition and object and scene classification. Researchers at the University of Chicago and partner institutions studying the foundations and applications of machine learning and AI. Advanced Algorithms. Besides providing an introduction to the software development process and the lifecycle of a software project, this course focuses on imparting a number of skills and industry best practices that are valuable in the development of large software projects, such as source control techniques and workflows, issue tracking, code reviews, testing, continuous integration, working with existing codebases, integrating APIs and frameworks, generating documentation, deployment, and logging and monitoring. Instructor(s): Staff Students will design and implement systems that are reliable, capable of handling huge amounts of data, and utilize best practices in interface and usability design to accomplish common bioinformatics problems. Prerequisite(s): CMSC 16100, or CMSC 15100 and by consent. Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). Email policy: We will prioritize answering questions posted to Ed Discussion, not individual emails. Certificate Program. CMSC27502. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. Data visualizations provide a visual setting in which to explore, understand, and explain datasets. CMSC21010. Letter grades will be assigned using the following hard cutoffs: A: 93% or higher Note(s): This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. It describes several important modern algorithms, provides the theoretical . Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Linear classifiers Numerical Methods. 100 Units. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. This is what makes the University of Chicago program uniquely fit to prepare students for their future.. Undergraduate Computational Linguistics. Introduction to Computer Science I. Note: students who earned a Pass or quality grade of D or better in CMSC 13600 may not enroll in CMSC 21800. Team projects are assessed based on correctness, elegance, and quality of documentation. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. Each topic will be introduced conceptually followed by detailed exercises focused on both prototyping (using matlab) and programming the key foundational algorithms efficiently on modern (serial and multicore) architectures. Students who major in computer science have the option to complete one specialization. Scalable systems are needed to collect, stream, process, and validate data at scale. Foundations of Machine Learning. This course leverages human-computer interaction and the tools, techniques, and principles that guide research on people to introduce you to the concepts of inclusive technology design. Graduate and undergraduate students will be expected to perform at the graduate level and will be evaluated equally. All students will be evaluated by regular homework assignments, quizzes, and exams. Operating Systems. files that use the command-line version of DrScheme. Systems Programming II. 100 Units. Two new projects will test out ways to make "intelligent" water [] Scientific Visualization. Formal constructive mathematics. 1. 100 Units. (Links to an external site.) 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