Kernel methods and support vector machines CMSC25460. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security Programming Proofs. 100 Units. 100 Units. Becca: Wednesdays 10:30-11:30AM, JCL 257, starting week of Oct. 7. 100 Units. 100 Units. Chicago, IL 60637 Scalable systems are needed to collect, stream, process, and validate data at scale. 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. Instructor(s): T. DupontTerms Offered: Autumn. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. 100 Units. A 20000-level course must replace each 10000-level course in the list above that was used to meet general education requirements or the requirements of a major. This thesis must be based on an approved research project that is directed by a faculty member and approved by the department counselor. We will explore these concepts with real-world problems from different domains. Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). Operating Systems. 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 Note(s): The prerequisites are under review and may change. 100 Units. The course examines in detail topics in both supervised and unsupervised learning. Class place and time: Mondays and Wednesdays, 3-4:15pm, Office hours: Mondays, 1:30-2:30pm when classes are in session, Piazza: https://piazza.com/uchicago/winter2019/cmsc25300/home, TAs: Zewei Chu, Alexander Hoover, Nathan Mull, Christopher Jones. This course introduces the fundamental concepts and techniques in data mining, machine learning, and statistical modeling, and the practical know-how to apply them to real-world data through Python-based software. 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. with William Howell. Prerequisite(s): CMSC 15400. This course deals with numerical linear algebra, approximation of functions, approximate integration and differentiation, Fourier transformation, solution of nonlinear equations, and the approximate solution of initial value problems for ordinary differential equations. Prerequisite(s): CMSC 12100 The class will also introduce students to basic aspects of the software development lifecycle, with an emphasis on software design. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. High-throughput automated biological experiments require advanced algorithms, implemented in high-performance computing systems, to interpret their results. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. See also some notes on basic matrix-vector manipulations. 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. Prerequisite(s): CMSC 15400 or CMSC 22000 100 Units. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. The course will involve a substantial programming project implementing a parallel computations. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. The courses will take students through the whole data science lifecycle, with all the concepts that they need to know: data collection, data engineering, programming, statistical inference, machine learning, databases, and issues around ethics, privacy and algorithmic transparency, Nicolae said. 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. Equivalent Course(s): DATA 11800, STAT 11800. Honors Theory of Algorithms. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. The numerical methods studied in this course underlie the modeling and simulation of a huge range of physical and social phenomena, and are being put to increasing use to an increasing extent in industrial applications. Terms Offered: Winter Summer In this course we will study the how machine learning is used in biomedical research and in healthcare delivery. Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. At the end of the sequence, she analyzed the rollout of COVID-19 vaccinations across different socioeconomic groups, and whether the Chicago neighborhoods suffering most from the virus received equitable access. Equivalent Course(s): CMSC 32900. Instead, we aim to provide the necessary mathematical skills to read those other books. CMSC27230. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the ), Zhuokai: Mondays 11am to 12pm, Location TBD. CMSC14300. Digital fabrication involves translation of a digital design into a physical object. Students may petition to have graduate courses count towards their specialization via this same page. Instructor(s): Michael MaireTerms Offered: Winter Advanced Database Systems. Scalar first-order hyperbolic equations will be considered. 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.. The centerpiece will be the new Data Science Clinic, a capstone, two-quarter sequence that places students on teams with public interest organizations, government agencies, industrial partners, and researchers. Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. Introduction to Computer Science II. Students will gain experience applying neural networks to modern problems in computer vision, natural language processing, and reinforcement learning. 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. Vectors and matrices in machine learning models Instructor(s): B. UrTerms Offered: Spring This class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer primarily to tabulated data). CMSC12100. The class will rigorously build up the two pillars of modern . Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. It requires a high degree of mathematical maturity, typical of mathematically-oriented CS and statistics PhD students or math graduates. Equivalent Course(s): MAAD 21111. Prerequisite(s): CMSC 25300 or CMSC 35300 or STAT 24300 or STAT 24500 It all starts with the University of Chicago vision for data science as an emerging new discipline, which will be reflected in the educational experience, said Michael J. Franklin, Liew Family Chairman of Computer Science and senior advisor to the Provost for computing and data science. Equivalent Course(s): MAAD 25300. Instructor(s): Feamster, NicholasTerms Offered: Winter Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. Actuated User Interfaces and Technology. The major requires five additional elective computer science courses numbered 20000 or above. Equivalent Course(s): DATA 25422, DATA 35422, CMSC 35422. More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. We will cover algorithms for transforming and matching data; hypothesis testing and statistical validation; and bias and error in real-world datasets. CMSC28130. 100 Units. This course will focus on analyzing complex data sets in the context of biological problems. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. CMSC27800. After successfully completing this course, a student should have the necessary foundation to quickly gain expertise in any application-specific area of computer modeling. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. UChicago Harris Campus Visit. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). increasing the total number of courses required in this category from two to three. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. The course will include bi-weekly programming assignments, a midterm examination, and a final. Introduction to Creative Coding. Students will complete weekly problem sets, as well as conduct novel research in a group capstone project. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. Is algorithmic bias avoidable? by | May 25, 2022 | fatal car accident in alvin, tx 2021 | catherine rusoff wikipedia | May 25, 2022 | fatal car accident in alvin, tx 2021 | catherine rusoff wikipedia 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. 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. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. 100 Units. Note: Students may petition to have graduate courses count towards their specialization. This concise review of linear algebra summarizes some of the background needed for the course. Part 1 covered by Mathematics for Machine Learning). The new paradigm of computing, harnessing quantum physics. Equivalent Course(s): MATH 28000. Techniques studied include the probabilistic method. We will build and explore a range of models in areas such as infectious disease and drug resistance, cancer diagnosis and treatment, drug design, genomics analysis, patient outcome prediction, medical records interpretation and medical imaging. Kernel methods and support vector machines CMSC 35300 Mathematical Foundations of Machine Learning; MACS 33002 Introduction to Machine Learning . Visit our page for journalists or call (773) 702-8360. Terms Offered: Autumn CMSC23320. Prerequisite(s): CMSC 15100 or CMSC 16100, and CMSC 27100 or CMSC 27700 or MATH 27700, or by consent. Instructor(s): A. DruckerTerms Offered: Winter By This course is the first in a pair of courses designed to teach students about systems programming. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. 100 Units. Mathematical Foundations of Machine Learning. 100 Units. Application: electronic health record analysis, Professor of Statistics and Computer Science, University of Chicago, Auto-differentiable Ensemble Kalman Filters, Pure exploration in kernel and neural bandits, Mathematical Foundations of Machine Learning (Fall 2021), https://piazza.com/uchicago/winter2019/cmsc25300/home, Matrix Methods in Data Mining and Pattern Recognition by Lars Elden, Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares. Lecture 1: Intro -- Mathematical Foundations of Machine Learning Equivalent Course(s): CMSC 33250. Relationships between space and time, determinism and non-determinism, NP-completeness, and the P versus NP question are investigated. Please retrieve the Zoom meeting links on Canvas. Application: electronic health record analysis, Professor of Statistics and Computer Science, University of Chicago, Auto-differentiable Ensemble Kalman Filters, Pure exploration in kernel and neural bandits, Mathematical Foundations of Machine Learning (Fall 2021), https://piazza.com/uchicago/fall2019/cmsc2530035300stat27700/home, https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/. 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 . Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. CMSC15200. This field is for validation purposes and should be left unchanged. The PDF will include all information unique to this page. Marti Gendel, a rising fourth-year, has used data science to support her major in biology. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. 2. TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. This course is offered in the Pre-College Summer Immersion program. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. This course deals with finite element and finite difference methods for second-order elliptic equations (diffusion) and the associated parabolic and hyperbolic equations. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) Basic counting is a recurring theme. Note(s): Prior experience with basic linear algebra (matrix algebra) is recommended. The textbooks will be supplemented with additional notes and readings. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. CMSC14200. The first phase of the course will involve prompts in which students design and program small-scale artworks in various contexts, including (1) data collected from web browsing; (2) mobility data; (3) data collected about consumers by major companies; and (4) raw sensor data. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Matlab, Python, Julia, or R). 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. We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Mathematical Foundations of Machine Learning. This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. To do so, students must take three courses from an approved list in lieu of three major electives. This is a practical programming course focused on the basic theory and efficient implementation of a broad sampling of common numerical methods. Homework exercises will give students hands-on experience with the methods on different types of data. Students will gain basic fluency with debugging tools such as gdb and valgrind and build systems such as make. Prerequisite(s): CMSC 22880 This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). CMSC29900. Instructor(s): B. SotomayorTerms Offered: Winter Students may petition to take more advanced courses to fulfill this requirement. 100 Units. We teach the "Unix way" of breaking a complex computational problem into smaller pieces, most or all of which can be solved using pre-existing, well-debugged, and documented components, and then composed in a variety of ways. PhD students in other departments, as well as masters students and undergraduates, with sufficient mathematical and programming background, are also welcome to take the course, at the instructors permission. From linear algebra and multivariate 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). Note(s): This course is offered in alternate years. This course covers the fundamentals of digital image formation; image processing, detection and analysis of visual features; representation shape and recovery of 3D information from images and video; analysis of motion. It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. Developing machine learning algorithms is easier than ever. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Ethics, Fairness, Responsibility, and Privacy in Data Science. Focuses specifically on deep learning and emphasizes theoretical and intuitive understanding. Introduction to Human-Computer Interaction. 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. Lecure 2: Vectors and matrices in machine learning notes, video, Lecture 3: Least squares and geometry notes, video, Lecture 4: Least squares and optimization notes, video, Lecture 5: Subspaces, bases, and projections notes, video, Lecture 6: Finding orthogonal bases notes, video, Lecture 7: Introduction to the Singular Value Decomposition notes video, Lecture 8: The Singular Value Decomposition notes video, Lecture 9: The SVD in Machine Learning notes video, Lecture 10: More on the SVD in Machine Learning (including matrix completion) notes video, Lecture 11: PageRank and Ridge Regression notes video, Lecture 12: Kernel Ridge Regression notes video, Lecture 13: Support Vector Machines notes video, Lecture 14: Basic Convex Optimization notes video, Lectures 15-16: Stochastic gradient descent and neural networks video 1, video 2, Lecture 17: Clustering and K-means notes video, This term we will be using Piazza for class discussion. Rising third-year Victoria Kielb has found surprising applications of data science through her work with the Robin Hood Foundation, the Chicago History Museum, and Facebook. Covering a story? His group developed mathematical models based on this data and then began using machine-learning methods to reveal new information about proteins' basic design rules. 100 Units. Prerequisite(s): CMSC 16100, or CMSC 15100 and by consent. Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Class discussion will also be a key part of the student experience. A grade of C- or higher must be received in each course counted towards the major. We will have several 3D printers available for use during the class and students will design and fabricate several parts during the course. Linear classifiers Model selection, cross-validation Instructor(s): B. SotomayorTerms Offered: Spring CMSC23240. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. The textbooks will be supplemented with additional notes and readings. This sequence can be in the natural sciences, social sciences, or humanities and sequences in which earlier courses are prerequisites for advanced ones are encouraged. 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. 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