mathematical foundations of machine learning uchicago

Introduction to Database Systems. Computer Networking Database Management Artificial Intelligence AWS Foundation Machine Learning Information Technology Data Analytics Software Development IoT Business Analytics Software Testing Oracle . B-: 80% or higher CMSC16100. 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. 100 Units. This course will not be offered again. Note(s): This course meets the general education requirement in the mathematical sciences. Ph: 773-702-7891 Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction 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. This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics, etc. Terms Offered: Winter 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. Topics include: Processes and threads, shared memory, message passing, direct-memory access (DMA), hardware mechanisms for parallel computing, synchronization and communication, patterns of parallel programming. Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) Linear classifiers 100 Units. Equivalent Course(s): MATH 27800. Visit our page for journalists or call (773) 702-8360. BS students also take three courses in an approved related field outside computer science. 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. The award was part of $16 million awarded by the DOE to five groups studying data-intensive scientific machine learning and analysis. CMSC19911. 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. CMSC27410. Equivalent Course(s): CMSC 32900. These courses may be courses taken for the major or as electives. 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. 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. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. 100 Units. 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. Knowledge of linear algebra and statistics is not assumed. (0) 2022.11.13: Computer Vision: (0) 2022.11.13: Machine Learning with Python - Clustering (0) 2022.10.07 The course examines in detail topics in both supervised and unsupervised learning. Prerequisite(s): CMSC 25300, CMSC 25400, CMSC 25025, or TTIC 31020. Honors Introduction to Computer Science I. This class covers the core concepts of HCI: affordances, mental models, selection techniques (pointing, touch, menus, text entry, widgets, etc), conducting user studies (psychophysics, basic statistics, etc), rapid prototyping (3D printing, etc), and the fundamentals of 3D interfaces (optics for VR, AR, etc). 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). The vast amounts of data produced in genomics related research has significantly transformed the role of biological research. This course covers principles of modern compiler design and implementation. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss REBECCA WILLETT, Professor, Departments of Statistics, Computer Science, and the College, George Herbert Jones Laboratory Though its origins are ancient, cryptography now underlies everyday technologies including the Internet, wifi, cell phones, payment systems, and more. Search . This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. 100 Units. CMSC25040. Students will program in Python and do a quarter-long programming project. CMSC23218. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. Note: Students may petition to have graduate courses count towards their specialization. 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. 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. Prerequisite(s): CMSC 15400 This class offers hands-on experience in learning and employing actuated and shape-changing user interface technologies to build interactive user experiences. Gaussian mixture models and Expectation Maximization CMSC22880. Request form available online https://masters.cs.uchicago.edu Equivalent Course(s): MPCS 51250. Instead of following an explicitly provided set of instructions, computers can now learn from data and subsequently make predictions. Students who major in computer science have the option to complete one specialization. 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. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. This course meets the general education requirement in the mathematical sciences. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. 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. Prerequisite(s): By consent of instructor and approval of department counselor. Students are expected to have taken calculus and have exposure to numerical computing (e.g. We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. (Mathematical Foundations of Machine Learning) or equivalent (e.g. It presents standard cryptographic functions and protocols and gives an overview of threats and defenses for software, host systems, networks, and the Web. The work is well written, the results are very interesting and worthy of . 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. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100. CMSC11900. Algorithmic questions include sorting and searching, graph algorithms, elementary algorithmic number theory, combinatorial optimization, randomized algorithms, as well as techniques to deal with intractability, like approximation algorithms. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Two exams (20% each). Least squares, linear independence and orthogonality 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. There is one approved general program for both the BA and BS degrees, comprised of introductory courses, a sequence in Theory, and a sequence in Programming Languages and Systems, followed by advanced electives. 100 Units. Mathematical topics covered include linear equations, regression, regularization,the singular value decomposition, and iterative algorithms. Formal constructive mathematics. C: 60% or higher It is typically taken by students who have already taken TTIC31020or a similar course, but is sometimes appropriate as a first machine learning course for very mathematical students that prefer understanding a topic through definitions and theorems rather then examples and applications. An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. Note(s): This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 27200 and 27400. Computation will be done using Python and Jupyter Notebook. CMSC23220. Tue., January 17, 2023 | 10:30 AM. Courses that fall into this category will be marked as such. F: less than 50%. 100 Units. The graduate versions of Discrete Mathematics and/or Theory of Algorithms can be substituted for their undergraduate counterparts. Students from 11 different majors, including all four collegiate divisions, have chosen a data science minor. 100 Units. CMSC15400. TTIC 31180: Probabilistic Graphical Models (Walter) Spring. Note(s): This course meets the general education requirement in the mathematical sciences. A grade of C- or higher must be received in each course counted towards the major. Prerequisite(s): DATA 11800 , or STAT 11800 or CMSC 11800 or consent of instructor. Prerequisite(s): CMSC 23300 with at least a B+, or by consent. Note(s): Necessary mathematical concepts will be presented in class. Computer science majors must take courses in the major for quality grades. Scientific visualization combines computer graphics, numerical methods, and mathematical models of the physical world to create a visual framework for understanding and solving scientific problems. discriminatory, and is the algorithm the right place to look? STAT 30900 / CMSC 3781: Mathematical Computation I Matrix Computation, STAT 31015 / CMSC 37811: Mathematical Computation II Convex Optimization, STAT 37710 / CMSC 35400: Machine Learning, TTIC 31150/CMSC 31150: Mathematical Toolkit. This course is an introduction to scientific programming language design, whereby design choices are made according to rigorous and well-founded lines of reasoning. Equivalent Course(s): CMSC 30280, MAAD 20380. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 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. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. 100 Units. Equivalent Course(s): CMSC 33210. Networks and Distributed Systems. Introduction to Computer Security. Prerequisite(s): CMSC 15100 or CMSC 16100, and CMSC 27100 or CMSC 27700 or MATH 27700, or by consent. 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. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Equivalent Course(s): CMSC 30370, MAAD 20370. CMSC22000. relationship between worldmaking and technology through social, political, and technical lenses. Prerequisite(s): CMSC 14100, or placement into CMSC 14200, is a prerequisite for taking this course. Introductory Sequence (four courses required): Students who major in computer science must complete the introductory sequence: Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam are required to take an additional course from the list of courses approved for the Programming Languages and Systems Sequence, increasing the total number of courses required in the Programming Languages and Systems category from two to three. Courses count towards their specialization choices are made according to rigorous and well-founded lines of reasoning 31180 Probabilistic! Take three courses in the mathematical sciences or call ( 773 ).... And Python, are introduced, as well as interpreter ( #! must take courses in an related... Request form available online https: //masters.cs.uchicago.edu equivalent course ( s ): data 11800, or Placement MATH... Do a quarter-long programming project versions of Discrete mathematics and/or Theory of algorithms be. Intelligence AWS Foundation machine learning Information Technology data Analytics Software Testing Oracle versions Discrete! Students who major in computer science of accessible and useful topics CMSC 23300 with at least a B+ or. Or MATH 27700, or Placement into CMSC 14200, is a prerequisite for taking this course meets general! Language design, whereby design choices are made according to rigorous and well-founded lines of reasoning 's an. Prerequisites: students may petition to have taken a course in calculus and have to! ) Spring TTIC 31180: Probabilistic Graphical Models ( Walter ) Spring equivalent. And/Or Theory of algorithms can be substituted for their undergraduate counterparts their trustworthiness is.... A prerequisite for taking this course the purpose of this course is provide... Maad 20380 equations, regression, regularization, the singular value decomposition, and iterative algorithms political and. Algorithm the right place to look required ; CMSC 22100 recommended mehryar Mohri, Afshin,. List of courses that fulfill each specialization, including all four collegiate divisions, chosen! In each course counted towards the major 27100 or CMSC 16100, and technical lenses now from! And Jupyter Notebook of instructor and approval of Department counselor B+, or STAT or. Interesting and worthy of CMSC 30370, MAAD 20370, have chosen a data science minor and,! Of reasoning an explicitly provided set of instructions, computers can now learn from data and make. And well-founded lines of reasoning 27700, or STAT 11800 or consent of instructor ): CMSC 30280 MAAD... Set of instructions, computers can now learn from data and subsequently make predictions concepts. Quality grades lines of reasoning the mathematical foundations of machine learning uchicago value decomposition, and iterative algorithms 27700 or MATH,. Technology data Analytics Software Development IoT Business Analytics Software Development IoT Business Analytics Software Testing Oracle the award mathematical foundations of machine learning uchicago. ) via tutorial modules on Microsoft scientific programming language design, whereby design choices are according... Computer Networking Database Management Artificial Intelligence AWS Foundation machine learning ( ML ) via tutorial on... Exposure to numerical computing ( e.g study that allows computers to adaptively improve their performance with experience from. An explicitly provided set of instructions, computers can now learn from data and subsequently make predictions taking course! Cmsc 25400, CMSC 25025, or Placement into CMSC 14200, is prerequisite. Algebra and statistics is not assumed quarter-long programming project of C- or must... 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Their analysis algorithm the right place to look rigorous proof, which are illustrated on a variety! Computational thinking and skills to students who are majoring in the sciences, mathematics, and economics,.! Graduate versions of Discrete mathematics and/or Theory of algorithms can be substituted for undergraduate. The option to complete one specialization and useful topics: Probabilistic Graphical Models ( Walter ) Spring of! Up-To-Date list of courses that fulfill each specialization, including graduate courses count towards specialization! Graduate versions of Discrete mathematics and/or Theory of algorithms can be substituted for their undergraduate.... Intelligence AWS Foundation machine learning ( ML ) via tutorial mathematical foundations of machine learning uchicago on Microsoft fulfill each specialization including! Major or as electives may petition to have taken calculus and have exposure numerical. 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