CMSC25610. Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Each topic will be introduced conceptually followed by detailed exercises focused on both prototyping (using matlab) and programming the key foundational algorithms efficiently on modern (serial and multicore) architectures. Instructor(s): A. RazborovTerms Offered: Autumn and two other courses from this list, Bachelors thesis in computer security, approved as such, Computer Systems: three courses from this list, over and above those taken to fulfill the programming languages and systems requirement, CMSC22240 Computer Architecture for Scientists, CMSC23300 Networks and Distributed Systems, CMSC23320 Foundations of Computer Networks, CMSC23500 Introduction to Database Systems, Bachelors thesis in computer systems, approved as such, Data Science: CMSC21800 Data Science for Computer Scientists and two other courses from this list, CMSC25025 Machine Learning and Large-Scale Data Analysis, CMSC25300 Mathematical Foundations of Machine Learning, Bachelors thesis in data science, approved as such, Human Computer Interaction:CMSC20300 Introduction to Human-Computer Interaction This course focuses on advanced concepts of database systems topics and assumes foundational knowledge outlined in CMSC 23500. 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. Labs expose students to software and hardware capabilities of mobile computing systems, and develop the capability to envision radical new applications for a large-scale course project. Note(s): Necessary mathematical concepts will be presented in class. Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. Least squares, linear independence and orthogonality Terms Offered: Winter Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. Please retrieve the Zoom meeting links on Canvas. Topics include (1) Statistical methods for large data analysis, (2) Parallelism and concurrency, including models of parallelism and synchronization primitives, and (3) Distributed computing, including distributed architectures and the algorithms and techniques that enable these architectures to be fault-tolerant, reliable, and scalable. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. 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 B: 83% or higher Introduction to Computer Science I-II. 100 Units. Students will also gain further fluency in working with the Linux command-line, including some basic operating system concepts, as well as the use of version control systems for collaborative software development. This course is centered around 3 mini projects exploring central concepts to robot programming and 1 final project whose topic is chosen by the students. The Lasso and proximal point algorithms - Financial Math at UChicago literally . Learning goals and course objectives. 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. To do so, students must choose three of their electives from the relevant approved specialization list. CMSC28400. Knowledge of linear algebra and statistics is not assumed. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. 100 Units. Understanding . 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. Instructor: Yuxin Chen . Instructor(s): B. SotomayorTerms Offered: Spring Theory of Algorithms. Students are required to submit the College Reading and Research Course Form. Email policy: The TAs and I will prioritize answering questions posted to Piazza, NOT individual emails. As such it has been a fertile ground for new statistical and algorithmic developments. 100 Units. 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. Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. ), Course Website: https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/, Ruoxi (Roxie) Jiang (Head TA), Lang Yu, Zhuokai Zhao, Yuhao Zhou, Takintayo (Tayo) Akinbiyi, Bumeng Zhuo. The final grade will be allocated to the different components as follows: Homework: 30%. Appropriate for graduate students or advanced undergraduates. Mobile Computing. Equivalent Course(s): LING 28610. UChicago students will have a wide variety of opportunities to engage projects across different sectors, disciplines and domains, from problems drawn from environmental and human rights groups to AI-driven finance and industry to cutting-edge research problems from the university, our national labs and beyond. Vectors and matrices in machine learning models Prerequisite(s): CMSC 15400. Programming projects will be in C and C++. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. 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. Solely based on the Online Introduction to Computer Science Exam students may be placed into: Students who place into CMSC 14200 will receive credit for CMSC14100 Introduction to Computer Science I upon successfully completing CMSC14200 Introduction to Computer Science II. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. This course covers the basics of the theory of finite graphs. Note: Students may petition to have graduate courses count towards their specialization. Students will program in Python and do a quarter-long programming project. Logistic regression Prerequisite(s): CMSC 25300, CMSC 25400, CMSC 25025, or TTIC 31020. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. Introduction to Data Engineering. To become a successful Data scientist, one should have skills in three major areas: Mathematics; Technology and Hacking; Strong Business Acumen Current focus areas include new techniques to capture 3d models (depth sensors, stereo vision), drones that enable targeted, adaptive, focused sensing, and new 3d interactive applications (augmented reality, cyberphysical, and virtual reality). The core theme for the Entrepreneurship in Technology course is that computer science students need exposure to the broad challenges of capturing opportunities and creating companies. Creative Machines and Innovative Instrumentation. Instructor(s): Michael MaireTerms Offered: Winter Digital fabrication involves translation of a digital design into a physical object. Prerequisite(s): Placement into MATH 13100 or higher, or by consent. "The urgency with which businesses need strong data science talent is rapidly increasing, said Kjersten Moody, AB98 and chief data officer at Prudential Financial. Introduction to Scientific Computing. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. Do predictive models violate privacy even if they do not use or disclose someone's specific data? Curriculum. CMSC16100. 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. Data Analytics. Programming languages often conflate the definition of mathematical functions, which deterministically map inputs to outputs, and computations that effect changes, such as interacting with users and their machines. 773.702.8333, University of Chicago Data Science Courses 2022-2023. Students will become familiar with the types and scale of data used to train and validate models and with the approaches to build, tune and deploy machine learned models. UChicago Financial Mathematics. Equivalent Course(s): MAAD 23220. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200, and the equivalent of two quarters of calculus (MATH 13200 or higher). CMSC12300. Cambridge University Press, 2020. Suite 222 100 Units. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. 5747 South Ellis Avenue This course covers the basics of computer systems from a programmer's perspective. Note(s): A more detailed course description should be available later. CMSC15100-15200. optional Introduction to Computer Science I. CMSC27620. 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. The graduate versions of Discrete Mathematics and/or Theory of Algorithms can be substituted for their undergraduate counterparts. Students will also gain basic facility with the Linux command-line and version control. Instructor(s): Allyson EttingerTerms Offered: Autumn This course focuses on the principles and techniques used in the development of networked and distributed software. *Students interested in theory or machine learning can replace CMSC14300 Systems Programming I and CMSC14400 Systems Programming II with 20000-level electives in those fields. Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source . Equivalent Course(s): MPCS 51250. Instructor(s): Stuart KurtzTerms Offered: TBD Note(s): anti-requisites: CMSC 25900, DATA 25900. Introduction to Neural Networks. 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. One central component of the program was formalizing basic questions in developing areas of practice and gaining fundamental insights into these. 100 Units. This course is the second quarter of a two-quarter systematic introduction to the foundations of data science, as well as to practical considerations in data analysis. Networks and Distributed Systems. Plan accordingly. Our emphasis is on basic principles, mathematical models, and efficient algorithms established in modern computer vision. 100 Units. 100 Units. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Experience with mathematical proofs. But the Introduction to Data Science sequence changed her view. increasing the total number of courses required in this category from two to three. Each subject is intertwined to develop our machine learning model and reach the "best" model for generalizing the dataset. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. 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. CMSC25440. Students should consult course-info.cs.uchicago.edufor up-to-date information. Students do reading and research in an area of computer science under the guidance of a faculty member. Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. 2022 6 - 2022 8 3 . Bookmarks will appear here. 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. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. This course covers education theory, psychology (e.g., motivation, engagement), and game design so that students can design and build an educational learning application. Reviewer 1 Report. Topics include lexical analysis, parsing, type checking, optimization, and code generation. Part 1 covered by Mathematics for Machine Learning). The article is an analysis of the current topic - digitalization of the educational process. Other topics include basic counting, linear recurrences, generating functions, Latin squares, finite projective planes, graph theory, Ramsey theory, coloring graphs and set systems, random variables, independence, expected value, standard deviation, and Chebyshev's and Chernoff's inequalities. A state-of-the-art research and teaching facility. BS students also take three courses in an approved related field outside computer science. Others serve supporting roles, such as part-of-speech tagging and syntactic parsing. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100. Reflecting the holistic vision for data science at UChicago, data science majors will also take courses in Ethics, Fairness, Responsibility, and Privacy in Data Science and the Societal Impacts of Data, exploring the intensifying issues surrounding the use of big data and analytics in medicine, policy, business and other fields. 100 Units. Equivalent Course(s): STAT 27725. Through multiple project-based assignments, students practice the acquired techniques to build interactive tangible experiences of their own. The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. 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.. In addition to his research, Veitch will teach courses on causality and machine learning as part of the new data science initiative at UChicago. Big Brains podcast: Is the U.S. headed toward another civil war? CMSC29512. 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. files that use the command-line version of DrScheme. 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. The course will provide an introduction to quantum computation and quantum technologies, as well as classical and quantum compiler techniques to optimize computations for technologies. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. CMSC22880. You will also put your skills into practice in a semester long group project involving the creation of an interactive system for one of the user populations we study. CMSC23240. Note(s): First year students are not allowed to register for CMSC 12100. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Linear classifiers Prerequisite(s): CMSC 15100, CMSC 16100, CMSC 12100, or CMSC 10500. 100 Units. 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. Rob Mitchum. 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. Instructor(s): Rick StevensTerms Offered: Autumn Operating Systems. Knowledge of linear algebra and statistics is not assumed. Please be aware that course information is subject to change, and the catalog does not necessarily reflect the most recent information. Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. 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. This course is an introduction to the mathematical foundations of machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising anddata analysis. This course introduces mathematical logic. The PDF will include all information unique to this page. CMSC27100. Modern machine learning techniques have ushered in a new era of computing. These scientific "miracles" are robust, and provide a valuable longer-term understanding of computer capabilities, performance, and limits to the wealth of computer scientists practicing data science, software development, or machine learning. 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. Prerequisite(s): CMSC 15400 Courses that fall into this category will be marked as such. CMSC23360. We will introduce the machine learning methods as we go, but previous familiarity with machine learning will be helpful. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. Mathematical topics covered include linear equations, regression, regularization,the singular value decomposition, and iterative algorithms. Matlab, Python, Julia, or R). - Bayesian Inference and Machine Learning I and II from Gordon Ritter. Software Construction. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. All students will be evaluated by regular homework assignments, quizzes, and exams. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. If you have any problems or feedback for the developers, email team@piazza.com. This course is an introduction to programming, using exercises in graphic design and digital art to motivate and employ basic tools of computation (such as variables, conditional logic, and procedural abstraction). Contacts | Program of Study | Where to Start | Placement | Program Requirements | Summary of Requirements | Specializations | Grading | Honors | Minor Program in Computer Science | Joint BA/MS or BS/MS Program | Graduate Courses | Schedule Changes | Courses, Department Website: https://www.cs.uchicago.edu. Foundations of Machine Learning. 100 Units. Placement into MATH 15100 or completion of MATH 13100. REBECCA WILLETT, Professor, Departments of Statistics, Computer Science, and the College, George Herbert Jones Laboratory 1427 East 60th Street The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. Instructor(s): S. KurtzTerms Offered: Spring Request form available online https://masters.cs.uchicago.edu CMSC 23206 Security, Privacy, and Consumer Protection, CMSC 25910 Engineering for Ethics, Privacy, and Fairness in Computer Systems, Bachelor's thesis in computer security, approved as such, CMSC 22240 Computer Architecture for Scientists, CMSC 23300 Networks and Distributed Systems, CMSC 23320 Foundations of Computer Networks, CMSC 23500 Introduction to Database Systems, CMSC 25422 Machine Learning for Computer Systems, Bachelor's thesis in computer systems, approved as such, CMSC 25025 Machine Learning and Large-Scale Data Analysis, CMSC 25300 Mathematical Foundations of Machine Learning, Bachelor's thesis in data science, approved as such, CMSC 20370 Inclusive Technology: Designing for Underserved and Marginalized Populations, CMSC 20380 Actuated User Interfaces and Technology, CMSC 23220 Inventing, Engineering and Understanding Interactive Devices, CMSC 23230 Engineering Interactive Electronics onto Printed Circuit Boards, CMSC 23240 Emergent Interface Technologies, CMSC 30370 Inclusive Technology: Designing for Underserved and Marginalized Populations, Bachelor's thesis in human computer interaction, approved as such, CMSC 25040 Introduction to Computer Vision, CMSC 25500 Introduction to Neural Networks, TTIC 31020 Introduction to Machine Learning, TTIC 31120 Statistical and Computational Learning Theory, TTIC 31180 Probabilistic Graphical Models, TTIC 31210 Advanced Natural Language Processing, TTIC 31220 Unsupervised Learning and Data Analysis, TTIC 31250 Introduction to the Theory of Machine Learning, Bachelor's thesis in machine learning, approved as such, CMSC 22600 Compilers for Computer Languages, Bachelor's thesis in programming languages, approved as such, CMSC 28000 Introduction to Formal Languages, CMSC 28100 Introduction to Complexity Theory, CMSC 28130 Honors Introduction to Complexity Theory, Bachelor's thesis in theory, approved as such. Feature functions and nonlinear regression and classification The Lasso and proximal point algorithms 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. CMSC13600. Students may not use AP credit for computer science to meet minor requirements. 100 Units. Some methods for solving linear algebraic systems will be used. Prerequisite(s): CMSC 15400 100 Units. Basic topics include processes, threads, concurrency, synchronization, memory management, virtual memory, segmentation, paging, caching, process and I/O scheduling, file systems, storage devices. CMSC20300. This course introduces the principles and practice of computer security. Instructor(s): S. LuTerms Offered: Autumn This course is an introduction to database design and implementation. 100 Units. Standard machine learning (ML) approaches often assume that the training and test data follow similar distributions, without taking into account the possibility of adversaries manipulating either distribution or natural distribution shifts. Honors Discrete Mathematics. CMSC11900. In this course, students will develop a deeper understanding of what a computer does when executing a program. Instructor(s): B. SotomayorTerms Offered: Winter Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110, or by consent. Instructor(s): B. UrTerms Offered: Spring The course will unpack and re-entangle computational connections and data-driven interactions between people, built space, sensors, structures, devices, and data. NLP includes a range of research problems that involve computing with natural language. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Functional Programming. Note(s): Prior experience with basic linear algebra (matrix algebra) is recommended. 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. CMSC 29700. Prerequisite(s): CMSC 15400. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. This course will explore the design, optimization, and verification of the software and hardware involved in practical quantum computer systems. Not allowed to register for CMSC 12100, or by consent will prioritize answering questions posted Piazza... Include lexical analysis, parsing, type checking, optimization, performance measurement, system-level I/O, iterative... As well as large-scale approaches to inference and machine Learning model training undergraduate counterparts data analysis used! 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