CMSC15100. CMSC23210. The topics covered in this course will include software, data mining, high-performance computing, mathematical models and other areas of computer science that play an important role in bioinformatics. 100 Units. This concise review of linear algebra summarizes some of the background needed for the course. Students will receive detailed feedback on their work from computer scientists, artists, and curators at the Museum of Science & Industry (MSI). Topics include lexical analysis, parsing, type checking, optimization, and code generation. CMSC29900. CMSC14200. Programming Languages: three courses from this list, over and above those courses taken to fulfill the programming languages and systems requirements, Theory: three courses from this list, over and above those taken to fulfill the theory requirements. Equivalent Course(s): MATH 28000. Instructor(s): Lorenzo OrecchiaTerms Offered: Spring They also allow us to formalize mathematics, stating and proving mathematical theorems in a manner that leaves no doubt as to their meaning or veracity. CMSC22300. Basic apprehension of calculus and linear algebra is essential. Introduction to Computer Security. Equivalent Course(s): MAAD 23220. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. 100 Units. CMSC20300. TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring. 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. CMSC22001. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Prerequisite(s): CMSC 23300 with at least a B+, or by consent. We expect this option to be attractive to a fair number of students from every major at UChicago, including the humanities, social sciences and biological sciences.. The course will be taught at an introductory level; no previous experience is expected. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. Boyd, Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares(available onlinehere) 100 Units. This course is an introduction to database design and implementation. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. This course is an introduction to key mathematical concepts at the heart of machine learning. Topics include: basic cryptography; physical, network, endpoint, and data security; privacy (including user surveillance and tracking); attacks and defenses; and relevant concepts in usable security. United States 100 Units. Courses that fall into this category will be marked as such. Features and models 100 Units. Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. Hardcover. Prerequisite(s): None Equivalent Course(s): MATH 28410. F: less than 50%. Introduction to Data Engineering. 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. Vectors and matrices in machine learning models Students will gain basic fluency with debugging tools such as gdb and valgrind and build systems such as make. The goal of this course is to provide a foundation for further study in computer security and to help better understand how to design, build, and use computer systems more securely. Random forests, bagging 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. No courses in the minor can be double counted with the student's major(s) or with other minors, nor can they be counted toward general education requirements. 5747 South Ellis Avenue It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Learnt data science, learn its content, discipline construction, applications and employment prospects. Helping someone suffering from schizophrenia determine reality; an alarm to help maintain distance during COVID; adding a fun gamification element to exercise. CMSC 23000 or 23300 recommended. CMSC 29700. A grade of C- or higher must be received in each course counted towards the major. 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. Students are expected to have taken calculus and have exposureto numerical computing (e.g. B: 83% or higher Prerequisite(s): MATH 25400 or 25700; open to students who are majoring in computer science who have taken CMSC 15400 along with MATH 16300 or MATH 16310 or Math 15910 or MATH 15900 or MATH 19900 This course will explore the design, optimization, and verification of the software and hardware involved in practical quantum computer systems. In this hands-on, practical course, you will design and build functional devices as a means to learn the systematic processes of engineering and fundamentals of design and construction. CMSC14300. Description: 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 and data analysis. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the Prerequisite(s): CMSC 15400 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. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. Prerequisite(s): CMSC 27100, CMSC 27130, or CMSC 37110, or MATH 20400 or MATH 20800. The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms. relationship between worldmaking and technology through social, political, and technical lenses. Computers for Learning. Courses that fall into this category will be marked as such. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. 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): William L Trimble / TBDTerms Offered: Spring The textbooks will be supplemented with additional notes and readings. This hands-on, authentic learning experience offers the real possibility for the field to grow in a manner that actually reflects the population it purports to engage, with diverse scientists asking novel questions from a wide range of viewpoints.. Non-majors may take courses either for quality grades or, subject to College regulations and with consent of the instructor, for P/F grading. CMSC28515. REBECCA WILLETT, Professor, Departments of Statistics, Computer Science, and the College, George Herbert Jones Laboratory arge software systems are difficult to build. Figure 4.1: An algorithmic framework for online strongly convex programming. This can lead to severe trustworthiness issues in ML. Sensing, actuation, and mediation capabilities of mobile devices are transforming all aspects of computing: uses, networking, interface, form, etc. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. Collaboration both within and across teams will be essential to the success of the project. 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. This course focuses on one intersection of technology and learning: computer games. Foundations Courses - 250 units. Students will complete weekly problem sets, as well as conduct novel research in a group capstone project. 100 Units. NOTE: 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 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. Note: students who earned a Pass or quality grade of D or better in CMSC 13600 may not enroll in CMSC 21800. UChicago (9) iversity (9) SAS Institute (9) . by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. 100 Units. CMSC12200. Waitlist: We will not be accepting auditors this quarter due to high demand. Machine Learning. Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Equivalent Course(s): STAT 37601. Mathematics for Machine Learning; by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Spring Instructor(s): Michael MaireTerms Offered: Winter CMSC16200. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. This course is an introduction to "big" data engineering where students will receive hands-on experience building and deploying realistic data-intensive systems. Note(s): Necessary mathematical concepts will be presented in class. Existing methods for analyzing genomes, sequences and protein structures will be explored, as well related computing infrastructure. This course includes a project where students will have to formulate hypotheses about a large dataset, develop statistical models to test those hypotheses, implement a prototype that performs an initial exploration of the data, and a final system to process the entire dataset. Topics include DBMS architecture, entity-relationship and relational models, relational algebra, concurrency control, recovery, indexing, physical data organization, and modern database systems. Prerequisite(s): Placement into MATH 13100 or higher, or by consent. Machine Learning: three courses from this list. CMSC23220. Honors Discrete Mathematics. The computer science minor must include three courses chosen from among all 20000-level CMSC courses and above. Download (official online versions from MIT Press): book ( PDF, HTML ). Do predictive models violate privacy even if they do not use or disclose someone's specific data? Defining this emerging field by advancing foundations and applications. Data Science for Computer Scientists. Advanced Algorithms. To earn a BA in computer science any sequence or pair of courses approved by the Physical Sciences Collegiate Division may be used to complete the general education requirement in the physical sciences. Please note that a course that is counted towards a specialization may not also be counted towards a major sequence requirement (i.e., Programming Languages and Systems, or Theory). 100 Units. Live. Instructor(s): ChongTerms Offered: Spring Prerequisite(s): CMSC 20300 Honors Theory of Algorithms. The honors version of Theory of Algorithms covers topics at a deeper level. To earn a BS in computer science, the general education requirement in the physical sciences must be satisfied by completing a two-quarter sequence chosen from the, BA: Any sequence or pair of courses that fulfills the general education requirement in the physical sciences, BS: Any two-quarter sequence that fulfills the general education requirement in the physical sciences for science majors, Programming Languages and Systems Sequence (two courses from the list below), Theory Sequence (three courses from the list below), Five electives numbered CMSC 20000 or above, BS (three courses in an approved program in a related field), Students who entered the College prior to Autumn Quarter 2022 and have already completed, CMSC 15200 will be offered in Autumn Quarter 2022, CMSC 15400 will be offered in Autumn Quarter 2022 and Winter Quarter 2023, increasing the total number of courses required in this category from two to three, for a total of six electives, as well as the, taken to fulfill the programming languages and systems requirements, Outstanding undergraduates may apply to complete an MS in computer science along with a BA or BS (generalized to "Bx") during their four years at the College. The minor adviser must approve the student's Consent to Complete a Minor Programform, and the student must submit that form to the student's College adviser by theend of Spring Quarter of the student's third year. Introduction to Computer Systems. Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. This course will examine how to design for security and privacy from a user-centered perspective by combining insights from computer systems, human-computer interaction (HCI), and public policy. 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). 100 Units. Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, the concepts of polynomial-time algorithms, and NP-completeness. C+: 77% or higher Reading and Research in Computer Science. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. 100 Units. This course will cover the principles and practice of security, privacy, and consumer protection. 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. Learning goals and course objectives. All rights reserved. Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. In the modern world, individuals' activities are tracked, surveilled, and computationally modeled to both beneficial and problematic ends. Students may not take CMSC 25910 if they have taken CMSC 25900 or DATA 25900. CMSC22240. (And how do we ensure this in the presence of failures?) UChicago Harris Campus Visit. This is what makes the University of Chicago program uniquely fit to prepare students for their future.. 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. 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. All students will be evaluated by regular homework assignments, quizzes, and exams. Search 209,580,570 papers from all fields of science. CMSC28130. Instead, C is developed as a part of a larger programming toolkit that includes the shell (specifically ksh), shell programming, and standard Unix utilities (including awk). Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. 100 Units. This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Over time, technology has occupied an increasing role in education, with mixed results. Features and models Prerequisite(s): CMSC 14300, or placement into CMSC 14400, is a prerequisite for taking this course. The statistical foundations of machine learning. Teaching staff: Lang Yu (TA); Yibo Jiang (TA); Jiedong Duan (Grader). Systems Programming I. Introduction to Computer Vision. Students who earn the BA are prepared either for graduate study in computer science or a career in industry. Students from 11 different majors, including all four collegiate divisions, have chosen a data science minor. Model selection, cross-validation CMSC23400. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. 100 Units. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation and communication of results. 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 class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer primarily to tabulated data). Application: text classification, AdaBoost Loss, risk, generalization Neural networks and backpropagation, Density estimation and maximum likelihood estimation The Elements of Statistical Learning (second edition); by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009. The class provides a range of basic engineering techniques to allow students to develop their own actuated user interface systems, including 3D mechanical design, digital fabrication (e.g. Advanced Distributed Systems. Scalable systems are needed to collect, stream, process, and validate data at scale. Computer Architecture for Scientists. Visit our page for journalists or call (773) 702-8360. The course is also intended for students outside computer science who are experienced with programming and computing with scientific data. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. 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. Terms Offered: Spring Note(s): This course meets the general education requirement in the mathematical sciences. and two other courses from this list, CMSC20370 Inclusive Technology: Designing for Underserved and Marginalized Populations, CMSC23220 Inventing, Engineering and Understanding Interactive Devices, CMSC23240 Emergent Interface Technologies, Bachelors thesis in human computer interaction, approved as such, Machine Learning: three courses from this list, CMSC25040 Introduction to Computer Vision, Bachelors thesis in machine learning, approved as such, Programming Languages: three courses from this list, over and above those coursestaken to fulfill the programming languages and systems requirements, CMSC22600 Compilers for Computer Languages, Bachelors thesis in programming languages, approved as such, Theory: three courses from this list, over and above those taken tofulfill the theory requirements, CMSC28000 Introduction to Formal Languages, CMSC28100 Introduction to Complexity Theory, CMSC28130 Honors Introduction to Complexity Theory, Bachelors thesis in theory, approved as such. Instructor(s): Rick StevensTerms Offered: Autumn 100 Units. Non-MPCS students must receive approval from program prior to registering. Note(s): This course is offered in alternate years. CMSC11800. This introduction to quantum computing will cover the key principles of quantum information science and how they relate to quantum computing as well as the notation and operations used in QIS. Least squares, linear independence and orthogonality Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). - Financial Math at UChicago literally . Sas Institute ( 9 ) simple techniques for data analysis are used to illustrate both effective and fallacious uses data., political, and probabilistic models build strength in an additional field by following approved! Consent of the instructor and Department counselor, including all four collegiate divisions, have chosen a data tools... 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