Topics include: algebraic datatypes, an elegant language for describing and manipulating domain-specific data; higher-order functions and type polymorphism, expressive mechanisms for abstracting programs; and a core set of type classes, with strong connections to category theory, that serve as a foundational and practical basis for mixing pure functions with stateful and interactive computations. The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. 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. 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. Ashley Hitchings never thought shed be interested in data science. Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. Prerequisite(s): CMSC 15200 or CMSC 16200. Students may not use AP credit for computer science to meet minor requirements. In this course, students will develop a deeper understanding of what a computer does when executing a program. These were just some of the innovative ideas presented by high school students who attended the most recent hands-on Broadening Participation in Computing workshop at the University of Chicago. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Students will be introduced to all of the biology necessary to understand the applications of bioinformatics algorithms and software taught in this course. Live. 100 Units. Instructor(s): Sarah SeboTerms Offered: Winter 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. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. The course will cover algorithms for symmetric-key and public-key encryption, authentication, digital signatures, hash functions, and other primitives. Machine Learning - Python Programming. Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) By using this site, you agree to its use of cookies. The new major is part of the University of Chicago Data Science Initiative, a coordinated, campus-wide plan to expand education, research, and outreach in this fast-growing field. This course focuses on one intersection of technology and learning: computer games. Students may petition to take more advanced courses to fulfill this requirement. Mobile computing is pervasive and changing nearly every aspect of society. Networks also help us understand properties of financial markets, food webs, and web technologies. I am delighted that data science will now join the ranks of our majors in the College, introducing students to the rigor and excitement of the higher learning.. In my opinion, this is the best book on mathematical foundations of machine learnign there is. Chicago, IL 60637 100 Units. We also discuss the Gdel completeness theorem, the compactness theorem, and applications of compactness to algebraic problems. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. Equivalent Course(s): MPCS 51250. 100 Units. Creative Coding. Simple type theory, strong normalization. Machine Learning. Functional Programming. This story was first published by the Department of Computer Science. 100 Units. Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. This policy allows you to miss class during a quiz or miss an assignment, but only one each. Bachelor's Thesis. CMSC25900. BS students also take three courses in an approved related field outside computer science. They also allow us to formalize mathematics, stating and proving mathematical theorems in a manner that leaves no doubt as to their meaning or veracity. CMSC23700. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). 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. The Computer Science Major Adviser is responsible for approval of specific courses and sequences, and responds as needed to changing course offerings in our program and other programs. Honors Introduction to Computer Science I-II. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. Actuated User Interfaces and Technology. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. We will explore these concepts with real-world problems from different domains. By We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the broadly, the computer science major (or minor). The course will place fundamental security and privacy concepts in the context of past and ongoing legal, regulatory, and policy developments, including: consumer privacy, censorship, platform content moderation, data breaches, net neutrality, government surveillance, election security, vulnerability discovery and disclosure, and the fairness and accountability of automated decision making, including machine learning systems. This course is an introduction to the design and analysis of cryptography, including how "security" is defined, how practical cryptographic algorithms work, and how to exploit flaws in cryptography. Instructor(s): Ketan MulmuleyTerms Offered: Autumn 100 Units. The statistical foundations of machine learning. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. The minor adviser must approve the student's Consent to Complete a Minor Programform, and the student must submit that form to the student's College adviser by theend of Spring Quarter of the student's third year. CMSC23300. Note(s): anti-requisites: CMSC 25900, DATA 25900. CMSC29512may not be used for minor credit. Equivalent Course(s): CMSC 32900. The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms. Prerequisite(s): CMSC 23300 with at least a B+, or by consent. CMSC22010. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. High-throughput automated biological experiments require advanced algorithms, implemented in high-performance computing systems, to interpret their results. Model selection, cross-validation The University of Chicago Booth School of Business Students are required to submit the College Reading and Research Course Form. CMSC12200. 100 Units. Introduction to Computer Science II. Each subject is intertwined to develop our machine learning model and reach the "best" model for generalizing the dataset. 100 Units. Systems Programming I. Topics include lexical analysis, parsing, type checking, optimization, and code generation. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). Focuses specifically on deep learning and emphasizes theoretical and intuitive understanding. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. CMSC23200. A written report is . Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. 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 for Computer Scientists. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, the concepts of polynomial-time algorithms, and NP-completeness. Students do reading and research in an area of computer science under the guidance of a faculty member. 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. Digital Fabrication. What is ML, how is it related to other disciplines? 100 Units. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Note(s): Students who have taken CMSC 15100 may take 16200 with consent of instructor. CMSC29900. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. 100 Units. As such it has been a fertile ground for new statistical and algorithmic developments. Opportunities for PhDs to work on world-class computer science research with faculty members. Linear classifiers Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets 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. Terms Offered: Winter Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. F: less than 50%. Prerequisite(s): CMSC 15400 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. The computer science program offers BA and BS degrees, as well as combined BA/MS and BS/MS degrees. CMSC28400. 100 Units. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. 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. 100 Units. 100 Units. Please retrieve the Zoom meeting links on Canvas. 100 Units. Networks help explain phenomena in such technological, social, and biological domains as the spread of opinions, knowledge, and infectious diseases. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. Application: text classification, AdaBoost Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Prerequisite(s): By consent of instructor and approval of department counselor. STAT 37750: Compressed Sensing (Foygel-Barber) Spring. By Louise Lerner, University of Chicago News Office As city populations boom and the need grows for sustainable energy and water, scientists and engineers with the University of Chicago and partners are looking towards artificial intelligence to build new systems to deal with wastewater. In this class we will engineer electronics onto Printed Circuit Boards (PCBs). Feature functions and nonlinear regression and classification discriminatory, and is the algorithm the right place to look? Prerequisite(s): CMSC 20300 Logistic regression Instructor(s): G. KindlmannTerms Offered: Winter Broadly speaking, Machine Learning refers to the automated identification of patterns in data. Prerequisite(s): CMSC 20300 or CMSC 20600 or CMSC 21800 or CMSC 22000 or CMSC 22001 or CMSC 23000 or CMSC 23200 or CMSC 23300 or CMSC 23320 or CMSC 23400 or CMSC 23500 or CMSC 23900 or CMSC 25025. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. AI approaches hold promise for improving models of climate and the universe, transforming waste products into energy sources, detecting new particles at the Large Hadron Collider, and countless . Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Features and models To do so, students must take three courses from an approved list in lieu of three major electives. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. 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. 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. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. We concentrate on a few widely used methods in each area covered. Mathematical Logic I-II. 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. CMSC23710. CMSC14400. Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. - "Online learning: theory, algorithms and applications ( . 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, Prerequisite(s): CMSC 15400. This course emphasizes the C Programming Language, but not in isolation. 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