With colleagues across the UChicago campus, the department also examines the considerable societal impacts and ethical questions of AI and machine learning, to ensure that the potential benefits of these approaches are not outweighed by their risks. The course examines in detail topics in both supervised and unsupervised learning. ), Zhuokai: Mondays 11am to 12pm, Location TBD. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, amortized analysis, analysis of probabilistic algorithms, the concepts of polynomial-time algorithms, and of NP-completeness. files that use the command-line version of DrScheme. 100 Units. Team projects are assessed based on correctness, elegance, and quality of documentation. The kinds of things you will learn may include mechanical design and machining, computer-aided design, rapid prototyping, circuitry, electrical measurement methods, and other techniques for resolving real-world design problems. Email policy: We will prioritize answering questions posted to Piazza, notindividual emails. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. This site uses cookies from Google to deliver its services and to analyze traffic. Terms Offered: Winter Basic machine learning methodology and relevant statistical theory will be presented in lectures. These tools have two main uses. Computer Science with Applications I-II-III. AI & Machine Learning Foundations and applications of computer algorithms making data-centric models, predictions, and decisions Modern machine learning techniques have ushered in a new era of computing. Model selection, cross-validation Download (official online versions from MIT Press): book ( PDF, HTML ). We'll explore creating a story, pitching the idea, raising money, hiring, marketing, selling, and more. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. 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 . Verification techniques to evaluate the correctness of quantum software and hardware will also be explored. The Curry-Howard Isomorphism. Ethics, Fairness, Responsibility, and Privacy in Data Science. 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. 100 Units. The National Science Foundation (NSF) Directorates for Computer and Information Science and Engineering (CISE), Engineering (ENG), Mathematical and Physical Sciences (MPS), and Social, Behavioral and Economic Sciences (SBE) promote interdisciplinary research in Mathematical and Scientific Foundations of Deep Learning and related areas (MoDL+). 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. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Features and models 100 Units. 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 . This course is an introduction to key mathematical concepts at the heart of machine learning. United States Instructor(s): Sarah SeboTerms Offered: Winter Instructor(s): Allyson EttingerTerms Offered: Autumn 100 Units. Students will design and implement systems that are reliable, capable of handling huge amounts of data, and utilize best practices in interface and usability design to accomplish common bioinformatics problems. 7750: Mathematical Foundations of Machine Learning (Fall 2022) Description: This course for beginning graduate students develops the mathematical foundations of machine learning, rigorously introducing students to modeling and representation, statistical inference, and optimization. Experience with mathematical proofs. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. The courses provided Hitchings with technical skills in programming, data analytics, statistical prediction and visualization, and allowed her to exercise that new toolset on real-world problems. Prerequisite(s): CMSC 15400 or CMSC 22000 CMSC23530. Least squares, linear independence and orthogonality Applications: recommender systems, PageRank, Ridge regression Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. Equivalent Course(s): MATH 28100. All students will be evaluated by regular homework assignments, quizzes, and exams. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. The honors version of Theory of Algorithms covers topics at a deeper level. Undergraduate Computational Linguistics. Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, Pattern Recognition and Machine Learning by Christopher Bishop, Mondays and Wednesdays, 9-10:20am in Crerar 011, Mondays and Wednesdays, 3-4:15pm in Ryerson 251. 100 Units. Students will also gain basic facility with the Linux command-line and version control. 100 Units. Prerequisite(s): (CMSC 12300 or CMSC 15400), or MAtH 16300 or higher, or by consent. Logistic regression 100 Units. 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. CMSC28100. Big Brains podcast: Is the U.S. headed toward another civil war? . Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT. 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. 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. There are roughly weekly homework assignments (about 8 total). CMSC 25025 Machine Learning and Large-Scale Data Analysis CMSC 25040 Introduction to Computer Vision CMSC 25300 Mathematical Foundations of Machine Learning CMSC 25400 Machine Learning CMSC 25440 Machine Learning in Medicine CMSC 25460 Introduction to Optimization CMSC 25500 Introduction to Neural Networks CMSC 25700 Natural Language Processing Prerequisite(s): CMSC 15400 CMSC13600. Instructor(s): Autumn Quarter Instructor: Scott WakelyTerms Offered: Autumn CMSC11111. 100 Units. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. Honors Combinatorics. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100. Prerequisite(s): By consent of instructor and approval of department counselor. Placement into MATH 15100 or completion of MATH 13100. 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. However, building and using these systems pose a number of more fundamental challenges: How do we keep the system operating correctly even when individual machines fail? Introduction to Creative Coding. Students with prior experience should plan to take the placement exam(s) (described below) to identify the appropriate place to start the sequence. Vectors and matrices in machine learning models 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. Foundations of Machine Learning The Program Workshops Internal Activities About T he goal of this program was to grow the reach and impact of computer science theory within machine learning. Logistic regression Students will gain basic fluency with debugging tools such as gdb and valgrind and build systems such as make. Mobile Computing. Note(s): This course is offered in alternate years. The Leibniz Institute SAFE is seeking to fill the position of a Research Assistant (m/f/d), 50% Position, salary group E13 TV-H. We are looking for a research assistant for the project "From Machine Learning to Machine Teaching (ML2MT) - Making Machines AND Humans Smarter" funded by Volkswagen Foundation with Prof. Pelizzon being one of . Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. In my opinion, this is the best book on mathematical foundations of machine learnign there is. More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. Courses fulfilling general education requirements must be taken for quality grades. CMSC23220. Students who place into CMSC14300 Systems Programming I will receive credit for CMSC14100 Introduction to Computer Science I and CMSC14200 Introduction to Computer Science II upon passing CMSC14300 Systems Programming I. This course is a direct continuation of CMSC 14100. Machine Learning for Finance . Decision trees CMSC28540. Solutions draw from machine learning (especially deep learning), algorithms, linguistics, and social sciences. Algorithmic questions include sorting and searching, discrete optimization, algorithmic graph theory, algorithmic number theory, and cryptography. Will also be explored range of machine learnign there is build upon tools. 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