Terms Offered: Winter In this class, we critically examine emergent technologies that might impact the future generations of computing interfaces, these include: physiological I/O (e.g., brain and muscle computer interfaces), tangible computing (giving shape and form to interfaces), wearable computing (I/O devices closer to the user's body), rendering new realities (e.g., virtual and augmented reality), haptics (giving computers the ability to generate touch and forces) and unusual auditory interfaces (e.g., silent speech and microphones as sensors). Foundations of Computer Networks. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) 3. Instructor(s): B. SotomayorTerms Offered: Spring Bachelor's Thesis. Terms Offered: Winter 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. CMSC13600. 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. A state-of-the-art research and teaching facility. Instructor(s): G. KindlmannTerms Offered: Winter Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. The course covers both the foundations of 3D graphics (coordinate systems and transformations, lighting, texture mapping, and basic geometric algorithms and data structures), and the practice of real-time rendering using programmable shaders. On the mathematical foundations of learning F. Cucker, S. Smale Published 5 October 2001 Computer Science Bulletin of the American Mathematical Society (1) A main theme of this report is the relationship of approximation to learning and the primary role of sampling (inductive inference). Note(s): Students who have taken CMSC 11800, STAT 11800, CMSC 12100, CMSC 15100, or CMSC 16100 are not allowed to register for CMSC 11111. Instructor(s): Stuart KurtzTerms Offered: TBD 100 Units. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Data science is more than a hot tech buzzword or a fashionable career; in the century to come, it will be an essential toolset in almost any field. Prerequisite(s): CMSC 23300 with at least a B+, or by consent. Students may petition to take more advanced courses to fulfill this requirement. 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.. CMSC28130. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. Instructor: Yuxin Chen . Two new projects will test out ways to make "intelligent" water [] Students are expected to have taken calculus and have exposure to numerical computing (e.g. 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. Equivalent Course(s): CMSC 33230. A broad background on probability and statistical methodology will be provided. Professor, Departments of Computer Science and Statistics, Assistant Professor, Department of Computer Science, Edward Carson Waller Distinguished Service Professor Emeritus, Departments of Computer Science and Linguistics, Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science, Assistant Professor, Department of Computer Science, College, Assistant Professor, Computer Science (starting Fall 2023), Associate Professor, Department of Computer Science, Associate Professor, Departments of Computer Science and Statistics, Associate Professor, Toyota Technological Institute, Professor, Toyota Technological Institute, Assistant Professor, Computer Science and Data Science, Assistant Professor, Toyota Technological Institute. This is a practical programming course focused on the basic theory and efficient implementation of a broad sampling of common numerical methods. Note Course #. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. Autumn/Spring. Prerequisite(s): CMSC 27100, CMSC 27130, or CMSC 37110, or MATH 20400 or MATH 20800. Data Visualization. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Students may not take CMSC 25910 if they have taken CMSC 25900 or DATA 25900. Exams (40%): Two exams (20% each). 100 Units. In this class we will engineer electronics onto Printed Circuit Boards (PCBs). CMSC12200. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. Artificial Intelligence, Algorithms and Human Rights. Knowledge of Java required. The major requires five additional elective computer science courses numbered 20000 or above. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. From linear algebra and multivariate CMSC22100. Part 1 covered by Mathematics for Machine Learning). CMSC15200. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. CMSC12100-12200-12300. UChicago CS studies all levels of machine learning and artificial intelligence, from theoretical foundations to applications in climate, data analysis, graphics, healthcare, networks, security, social sciences, and interdisciplinary scientific discovery. A-: 90% or higher Prerequisite(s): CMSC 15400 or CMSC 22000 Topics covered will include applications of machine learning models to security, performance analysis, and prediction problems in systems; data preparation, feature selection, and feature extraction; design, development, and evaluation of machine learning models and pipelines; fairness, interpretability, and explainability of machine learning models; and testing and debugging of machine learning models. This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Instructor(s): Lorenzo OrecchiaTerms Offered: Spring This course covers education theory, psychology (e.g., motivation, engagement), and game design so that students can design and build an educational learning application. Students will be expected to actively participate in team projects in this course. CMSC22001. Note(s): Prior experience with basic linear algebra (matrix algebra) is recommended. Emergent Interface Technologies. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. 100 Units. Topics include automata theory, regular languages, context-free languages, and Turing machines. Prerequisite(s): CMSC 22880 In this course, students will learn the fundamental principles, techniques, and tradeoffs in designing the hardware/software interface and hardware components to create a computing system that meets functional, performance, energy, cost, and other specific goals. D: 50% or higher 2022 6 - 2022 8 3 . Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. 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. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. This site uses cookies from Google to deliver its services and to analyze traffic. The article is an analysis of the current topic - digitalization of the educational process. This exam will be offered in the summer prior to matriculation. CMSC23320. Instructor(s): Sarah SeboTerms Offered: Winter 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. Machine learning topics include thelasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks,and deep learning. 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. Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). An understanding of the techniques, tricks, and traps of building creative machines and innovative instrumentation is essential for a range of fields from the physical sciences to the arts. Broadly speaking, Machine Learning refers to the automated identification of patterns in data. Her experience in Introduction to Data Science not only showed her how to use these tools in her research, but also how to effectively evaluate how other scientists deploy data science, AI and other approaches. Foundations of Machine Learning. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. The course examines in detail topics in both supervised and unsupervised learning. Prerequisite(s): CMSC 15200 or CMSC 16200. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Students who entered the College prior to Autumn Quarter 2022 and have already completedpart of the recently retired introductory sequence(CMSC12100 Computer Science with Applications I, CMSC15100 Introduction to Computer Science I,CMSC15200 Introduction to Computer Science II, and/or CMSC16100 Honors Introduction to Computer Science I) should plan to follow the academic year 2022 catalog. Prerequisite(s): CMSC 27100 or CMSC 27130, or MATH 15900 or MATH 19900 or MATH 25500; experience with mathematical proofs. Many of these fundamental problems were identified and solved over the course of several decades, starting in the 1970s. 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. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. 100 Units. CMSC23300. We split the book into two parts: Mathematical foundations; Example machine learning algorithms that use the mathematical foundations This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. Prerequisite(s): CMSC 15400 100 Units. Big Brains podcast: Is the U.S. headed toward another civil war? Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. Instructor(s): Allyson EttingerTerms Offered: Autumn arge software systems are difficult to build. Placement into MATH 15100 or completion of MATH 13100. Topics covered include two parts: (1) a gentle introduction of machine learning: generalization and model selection, regression and classification, kernels, neural networks, clustering and dimensionality reduction; (2) a statistical perspective of machine learning, where we will dive into several probabilistic supervised and unsupervised models, including logistic regression, Gaussian mixture models, and generative adversarial networks. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. Computer Science with Applications III. (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. Summer Chicago, IL 60637 Visualizations will be primarily web-based, using D3.js, and possibly other higher-level languages and libraries. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. Equivalent Course(s): MATH 27700. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. The rst half of the book develops Boolean type theory | a type-theoretic formal foundation for mathematics designed speci cally for this course. 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. 100 Units. - Bayesian Inference and Machine Learning I and II from Gordon Ritter. CMSC27502. By Equivalent Course(s): ASTR 21400, ASTR 31400, PSMS 31400, CHEM 21400, PHYS 21400. 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. 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. Compilers for Computer Languages. UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019 UChicago STAT 31140: Computational Imaging Theory and Methods UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019 UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017 In order to make the operations of the computer more transparent, students will study the C programming language, with special attention devoted to bit-level programming, pointers, allocation, file input and output, and memory layout. Introduction to Database Systems. UChicago Financial Mathematics. Boyd, Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares(available onlinehere) Students do reading and research in an area of computer science under the guidance of a faculty member. CMSC20370. Note(s): This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 27200 and 27400. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) 100 Units. provided on Canvas). Plan accordingly. Bookmarks will appear here. About this Course. 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. 100 Units. Instructor(s): S. KurtzTerms Offered: Spring Reading and Research in Computer Science. Letter grades will be assigned using the following hard cutoffs: A: 93% or higher The objective is that everyone creates their own, custom-made, functional I/O device. Students should consult the major adviser with questions about specific courses they are considering taking to meet the requirements. Extensive programming required. 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