Computer Science with Applications I. This course is a basic introduction to computability theory and formal languages. Students may also earn a BA or BS degree with honors by attaining the same minimum B grade in all courses in the major and by writing a successful bachelor's thesis as part of CMSC29900 Bachelor's Thesis. 100 Units. Introduction to Data Science I. Probabilistic Machine Learning: An Introduction; by Kevin Patrick Murphy, MIT Press, 2021. Prerequisite(s): (CMSC 12200 or CMSC 15200 or CMSC 16200) and (CMSC 27200 or CMSC 27230 or CMSC 37000). Opportunities for PhDs to work on world-class computer science research with faculty members. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. In total, the Financial Mathematics degree requires the successful completion of 1250 units. At what level does an entering student begin studying computer science at the University of Chicago? Least squares, linear independence and orthogonality Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. CMSC22000. This course is a direct continuation of CMSC 14100. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Kernel methods and support vector machines Least squares, linear independence and orthogonality Youshould make the request for Pass/Fail grading in writing (private note on Piazza). This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Equivalent Course(s): CMSC 30370, MAAD 20370. Type a description and hit enter to create a bookmark; 3. Mathematical Logic II. Students will gain basic fluency with debugging tools such as gdb and valgrind and build systems such as make. Rob Mitchum. 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. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. 100 Units. Download (official online versions from MIT Press): book ( PDF, HTML ). Both courses in this sequence meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15200 or 16200 to meet requirements for the major. Prerequisite(s): MATH 25400 or 25700; open to students who are majoring in computer science who have taken CMSC 15400 along with MATH 16300 or MATH 16310 or Math 15910 or MATH 15900 or MATH 19900 Equivalent Course(s): MATH 27800. Team projects are assessed based on correctness, elegance, and quality of documentation. 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 General Education Sequences for Science Majors. Artificial Intelligence, Algorithms and Human Rights. Bachelor's Thesis. Quantum Computer Systems. 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. 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 University of Chicago Booth School of Business Sec 02: MW 9:00 AM-10:20AM in Crerar Library 011, Textbook(s): Eldn,Matrix Methods in Data Mining and Pattern Recognition(recommended). B-: 80% or higher Faculty-led research groups exploring research areas within computer science and its interdisciplinary applications. Neural networks and backpropagation, Density estimation and maximum likelihood estimation 2. The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. UChicago Financial Mathematics. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. 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. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. Winter This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. 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. Prerequisite(s): CMSC 12100 The course will be fast moving and will involve weekly program assignments. Students should consult course-info.cs.uchicago.edufor up-to-date information. Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. All paths prepare students with the toolset they need to apply these skills in academia, industry, nonprofit organizations, and government. CMSC23000. While digital fabrication has been around for decades, only now has it become possible for individuals to take advantage of this technology through low cost 3D printers and open source tools for 3D design and modeling. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. Machine Learning: three courses from this list. The College and the Department of Computer Science offer two placement exams to help determine the correct starting point: The Online Introduction to Computer Science Exam may be taken (once) by entering students or by students who entered the College prior to Summer Quarter 2022. We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Please be aware that course information is subject to change, and the catalog does not necessarily reflect the most recent information. This course explores new technologies driving mobile computing and their implications for systems and society. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. This hands-on, authentic learning experience offers the real possibility for the field to grow in a manner that actually reflects the population it purports to engage, with diverse scientists asking novel questions from a wide range of viewpoints.. To become a successful Data scientist, one should have skills in three major areas: Mathematics; Technology and Hacking; Strong Business Acumen Advanced Algorithms. 100 Units. Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). hold zoom meetings, where you can participate, ask questions directly to the instructor. 100 Units. It provides a systematic introduction to machine learning and survey of a wide range of approaches and techniques. Title: Mathematical Foundations of Machine Learning, Teaching Assistant(s): Takintayo Akinbiyi and Bumeng Zhuo, ClassSchedule: Sec 01: MW 3:00 PM4:20 PM in Ryerson 251 Loss, risk, generalization Introduction to Computer Security. This course covers the basics of the theory of finite graphs. The course will be organized primarily around the development of a class-wide software project, with students organized into teams. CMSC21010. C: 60% or higher This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. In these opportunities, Kielb utilized her data science toolkit to analyze philanthropic dollars raised for a multi-million dollar relief fund; evaluate how museum members of different ages respond to virtual programming; and generate market insights for a product in its development phase. Terms Offered: Spring We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. 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. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. When she arrived at the University of Chicago, she was passionate about investigative journalism and behavioral economics, with a focus on narratives over number-crunching. Masters Program in Computer Science (MPCS), Masters in Computational Analysis and Public Policy (MSCAPP), Equity, Diversity, and Inclusion (EDI) Committee, SAND (Security, Algorithms, Networking and Data) Lab, Network Operations and Internet Security (NOISE) Lab, Strategic IntelliGence for Machine Agents (SIGMA) Lab. Contacts | Program of Study | Where to Start | Placement | Program Requirements | Summary of Requirements | Specializations | Grading | Honors | Minor Program in Computer Science | Joint BA/MS or BS/MS Program | Graduate Courses | Schedule Changes | Courses, Department Website: https://www.cs.uchicago.edu. 100 Units. 100 Units. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. 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. CMSC19911. Students who have taken CMSC 23300 may not take CMSC 23320. Vectors and matrices in machine learning models Machine Learning and Algorithms | Financial Mathematics | The University of Chicago Home / Curriculum / Machine Learning and Algorithms Machine Learning and Algorithms 100 Units Needed for Degree Completion Any Machine Learning and Algorithms Courses taken in excess of 100 units count towards the Elective requirement. MIT Press, Second Edition, 2018. 100 Units. UChicago Harris Campus Visit. Note(s): The prerequisites are under review and may change. (Mathematical Foundations of Machine Learning) or equivalent (e.g. This site uses cookies from Google to deliver its services and to analyze traffic. Equivalent Course(s): STAT 11900, DATA 11900. Introduction to Neural Networks. What makes an algorithm Note(s): This course meets the general education requirement in the mathematical sciences. 100 Units. What is ML, how is it related to other disciplines? Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Machine learning topics include thelasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks,and deep learning. I'm confident the University of Chicago data science major, with the innovative clinic model, will produce well-rounded graduates who will thrive in any industry. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. Dependent types. This course covers the basics of computer systems from a programmer's perspective. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. Instructor(s): LopesTerms Offered: Spring This course is a direct continuation of CMSC 14300. 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. She joined the CSU faculty in 2013 after obtaining dual B.S. 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. Reading and Research in Computer Science. This course also includes hands-on labs, where students will enhance their learning by implementing a modern microprocessor in a C simulator. The Curry-Howard Isomorphism. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. 100 Units. Introduction to Computer Science II. Terms Offered: Autumn Gaussian mixture models and Expectation Maximization 100 Units. Application: text classification, AdaBoost Introduction to Creative Coding. Linear classifiers Parallel Computing. Students can find more information about this course at http://bit.ly/cmsc12100-aut-20. A range of data types and visual encodings will be presented and evaluated. 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 . Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. They allow us to prove properties of our programs, thereby guaranteeing that our code is free of software errors. The ideal student in this course would have a strong interest in the use of computer modeling as predictive tool in a range of discplines -- for example risk management, optimized engineering design, safety analysis, etc. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. that at most one of CMSC 25500 and TTIC 31230 count Gaussian mixture models and Expectation Maximization Students will receive detailed feedback on their work from computer scientists, artists, and curators at the Museum of Science & Industry (MSI). This course is the first in a pair of courses designed to teach students about systems programming. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. Plan accordingly. 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. 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. Students will be introduced to all of the biology necessary to understand the applications of bioinformatics algorithms and software taught in this course. Does human review of algorithm sufficient, and in what cases? 100 Units. The class will also introduce students to basic aspects of the software development lifecycle, with an emphasis on software design. CMSC22001. Prerequisite(s): (CMSC 12300 or CMSC 15400), or MAtH 16300 or higher, or by consent. 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. Basic counting is a recurring theme. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. Prerequisite(s): CMSC 15400. 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