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. Winter Systems Programming I. Students will also gain further fluency in working with the Linux command-line, including some basic operating system concepts, as well as the use of version control systems for collaborative software development. CMSC25025. Semantic Scholar's Logo. CMSC20380. The graduate versions of Discrete Mathematics and/or Theory of Algorithms can be substituted for their undergraduate counterparts. Students are expected to have taken calculus and have exposure to numerical computing (e.g. STAT 37500: Pattern Recognition (Amit) Spring. Honors Theory of Algorithms. CMSC25040. The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. The course discusses both the empirical aspects of software engineering and the underlying theory. CMSC12300. You can read more about Prof. Rigollet's work and courses [on his . There is one approved general program for both the BA and BS degrees, comprised of introductory courses, a sequence in Theory, and a sequence in Programming Languages and Systems, followed by advanced electives. Bookmarks will appear here. The goal of this course is to provide a foundation for further study in computer security and to help better understand how to design, build, and use computer systems more securely. B-: 80% or higher This course covers computational methods for structuring and analyzing data to facilitate decision-making. for a total of six electives, as well as theadditional Programming Languages and Systems Sequence course mentioned above. STAT 37400: Nonparametric Inference (Lafferty) Fall. This policy allows you to miss class during a quiz or miss an assignment, but only one each. CMSC16100. More than half of the requirements for the minor must be met by registering for courses bearing University of Chicago course numbers. Instructor(s): G. KindlmannTerms Offered: Winter More events. Advanced Distributed Systems. Prerequisite(s): CMSC 15400 The course will be fast moving and will involve weekly program assignments. Prerequisite(s): CMSC 14100, or placement into CMSC 14200, is a prerequisite for taking this course. The honors version of Theory of Algorithms covers topics at a deeper level. Reviewer 1 Report. Note(s): Necessary mathematical concepts will be presented in class. 100 Units. Opportunities for PhDs to work on world-class computer science research with faculty members. Introduction to Complexity Theory. TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring. 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. Introduction to Computer Science I-II. Learning goals and course objectives. Prerequisite(s): CMSC 25300 or CMSC 35300 or STAT 24300 or STAT 24500 Networks and Distributed Systems. Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. 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. Mathematical Foundations of Machine Learning. CMSC21400. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. 100 Units. 100 Units. Instead, C is developed as a part of a larger programming toolkit that includes the shell (specifically ksh), shell programming, and standard Unix utilities (including awk). Prerequisite(s): CMSC 16100, or CMSC 15100 and by consent. For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. CMSC23010. 3. 5747 South Ellis Avenue CMSC14300. Each of these mini projects will involve students programming real, physical robots interacting with the real world. 100 Units. 100 Units. 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. Terms Offered: Winter This concise review of linear algebra summarizes some of the background needed for the course. 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. Introduction to Computer Vision. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Quizzes will be via canvas and cover material from the past few lectures. No previous biology coursework is required or expected. In recent years, large distributed systems have taken a prominent role not just in scientific inquiry, but also in our daily lives. 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. Teaching staff: Lang Yu (TA); Yibo Jiang (TA); Jiedong Duan (Grader). We compliment the lectures with weekly programming assignments and two larger projects, in which we build/program/test user-facing interactive systems. Terms Offered: Spring This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. hold zoom meetings, where you can participate, ask questions directly to the instructor. Equivalent Course(s): LING 28610. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Proficiency in Python is expected. In addition to small and medium sized programming assignments, the course includes a larger open-ended final project. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. Students do reading and research in an area of computer science under the guidance of a faculty member. Students can find more information about this course at http://bit.ly/cmsc12100-aut-20. Basic data structures, including lists, binary search trees, and tree balancing. NOTE: 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. The textbooks will be supplemented with additional notes and readings. Note: students can use at most one of CMSC 25500 and TTIC 31230 towards the computer science major. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. Equivalent Course(s): CMSC 33710. 100 Units. A major goal of this course is to enable students to formalize and evaluate theoretical claims. Scalar first-order hyperbolic equations will be considered. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Big Brains podcast: Is the U.S. headed toward another civil war? 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). The Lasso and proximal point algorithms Prerequisite(s): CMSC 15400. Exams: 40%. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. By Topics include data representation, machine language programming, exceptions, code optimization, performance measurement, memory systems, and system-level I/O. The course will involve a substantial programming project implementing a parallel computations. 100 Units. These include linear and logistic regression and . 100 Units. Honors Introduction to Complexity Theory. CMSC11000. The textbooks will be supplemented with additional notes and readings. 100 Units. After successfully completing this course, a student should have the necessary foundation to quickly gain expertise in any application-specific area of computer modeling. Students from 11 different majors, including all four collegiate divisions, have chosen a data science minor. 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. 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. At what level does an entering student begin studying computer science at the University of Chicago? An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. Students must be admitted to the joint MS program. Successfully created an ML model with Python and Azure, which can predict whether or not a . CMSC23400. Mobile computing is pervasive and changing nearly every aspect of society. CMSC 35300 Mathematical Foundations of Machine Learning; MACS 33002 Introduction to Machine Learning . Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. 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. . No matter where I go after graduation, I can help make sense of chaos in whatever kind of environment I'm working in.. Through multiple project-based assignments, students practice the acquired techniques to build interactive tangible experiences of their own. Topics include program design, control and data abstraction, recursion and induction, higher-order programming, types and polymorphism, time and space analysis, memory management, and data structures including lists, trees, and graphs. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Applications: recommender systems, PageRank, Ridge regression Computer Science with Applications I. Entrepreneurship in Technology. In this course, we will explore the use of proof assistants, computer programs that allow us to write, automate, and mechanically check proofs. Note(s): This course is offered in alternate years. Machine Learning for Finance . Computer Science with Applications III. One central component of the program was formalizing basic questions in developing areas of practice and gaining fundamental insights into these. 100 Units. CMSC11800. In their book, there are math foundations that are important for Machine Learning. 100 Units. Machine Learning in Medicine. CMSC23900. 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.. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. Equivalent Course(s): MPCS 54233. Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. 1. Scientific Visualization. Vectors and matrices in machine learning models Programming Languages: three courses from this list, over and above those courses taken to fulfill the programming languages and systems requirements, Theory: three courses from this list, over and above those taken to fulfill the theory requirements. The objective is that everyone creates their own, custom-made, functional I/O device. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. Prerequisite(s): CMSC 14300, or placement into CMSC 14400, is a prerequisite for taking this course. We will use traditional machine learning methods as well as deep learning depending on the problem. CMSC16200. From linear algebra and multivariate Email policy: We will prioritize answering questions posted to Ed Discussion, not individual emails. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. Students will be able to choose from multiple tracks within the data science major, including a theoretical track, a computational track and a general track balanced between the . degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont . Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. 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. Operating Systems. Instead, we aim to provide the necessary mathematical skills to read those other books. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. CMSC16100-16200. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. Students may petition to take more advanced courses to fulfill this requirement. 100 Units. Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) Foundations of Machine Learning. Exams (40%): Two exams (20% each). This course focuses on advanced concepts of database systems topics and assumes foundational knowledge outlined in CMSC 23500. CMSC23210. Equivalent Course(s): CMSC 30280, MAAD 20380. The honors version of Discrete Mathematics covers topics at a deeper level. Students will become familiar with the types and scale of data used to train and validate models and with the approaches to build, tune and deploy machine learned models. Information about your use of this site is shared with Google. Gaussian mixture models and Expectation Maximization Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) Prerequisite(s): CMSC 15400 and one of CMSC 22200, CMSC 22600, CMSC 22610, CMSC 23300, CMSC 23400, CMSC 23500, CMSC 23700, CMSC 27310, or CMSC 23800 strongly recommended. We designed the major specifically to enable students who want to combine data science with another B.A., Biron said. Mathematical Foundations of Machine Learning. 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: Spring Equivalent Course(s): CAPP 30350, CMSC 30350. Current focus areas include new techniques to capture 3d models (depth sensors, stereo vision), drones that enable targeted, adaptive, focused sensing, and new 3d interactive applications (augmented reality, cyberphysical, and virtual reality). 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. Data visualizations provide a visual setting in which to explore, understand, and explain datasets. Programming projects will be in C and C++. In addition to his research, Veitch will teach courses on causality and machine learning as part of the new data science initiative at UChicago. Any 20000-level computer science course taken as an elective beyond requirements for the major may, with consent of the instructor, be taken for P/F grading. Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. 100 Units. Prerequisite(s): By consent of instructor and approval of department counselor. Develops data-driven systems that derive insights from network traffic and explores how network traffic can reveal insights into human behavior. 100 Units. Church's -calculus, -reduction, the Church-Rosser theorem. Learnt data science, learn its content, discipline construction, applications and employment prospects. Loss, risk, generalization The math subject is: Image created by Author Six math subjects become the foundation for machine learning. Prerequisite(s): CMSC 15400. Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI. The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. Weekly problem sets will include both theoretical problems and programming tasks. 1. Students who are placed into CMSC14300 Systems Programming I will be invited to sit for the Systems Programming Exam, which will be offered later in the summer. Programming Languages and Systems Sequence (two courses required): Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam must replace it with an additional course from this list, Mathematical Foundations of Option Pricing . Prerequisite(s): CMSC 15400 or CMSC 22000 Note: Students may petition to have graduate courses count towards their specialization. Defining this emerging field by advancing foundations and applications. 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. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. Machine learning algorithms are also used in data modeling. Students should consult the major adviser with questions about specific courses they are considering taking to meet the requirements. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. 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. Prerequisite(s): CMSC 23300 or CMSC 23320 Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. CMSC23530. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. Honors Combinatorics. This course covers principles of modern compiler design and implementation. CMSC21010. Terms Offered: Winter Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. MIT Press, Second Edition, 2018. Terms Offered: Spring The major requires five additional elective computer science courses numbered 20000 or above. It made me realize how powerful data science is in drawing meaningful conclusions and promoting data-driven decision-making, Kielb said. Equivalent Course(s): MATH 28410. Features and models This course will cover the principles and practice of security, privacy, and consumer protection. Digital fabrication involves translation of a digital design into a physical object. - "Online learning: theory, algorithms and applications ( . The course relies on a good math background, as can be expected from a CS PhD student. Point algorithms prerequisite ( s ): CMSC 15400 the course may 16-17, 2023 UIC. 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