Topics include propositional and predicate logic and the syntactic notion of proof versus the semantic notion of truth (e.g., soundness, completeness). When does nudging violate political rights? CMSC25440. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. 100 Units. Equivalent Course(s): MATH 27800. This can lead to severe trustworthiness issues in ML. 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. 100 Units. Students who major in computer science have the option to complete one specialization. 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. 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. 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. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). Final: TBD. To become a successful Data scientist, one should have skills in three major areas: Mathematics; Technology and Hacking; Strong Business Acumen Equivalent Course(s): STAT 27725. Basic counting is a recurring theme. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. increasing the total number of courses required in this category from two to three. Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. CMSC23300. The course will be organized primarily around the development of a class-wide software project, with students organized into teams. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. 100 Units. lecture slides . (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. 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. - Bayesian Inference and Machine Learning I and II from Gordon Ritter. 100 Units. Instructor(s): S. LuTerms Offered: Autumn The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. Students will gain further fluency with debugging tools and build systems. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. The course project will revolve around the implementation of a mini x86 operating system kernel. 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. 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). Prerequisite(s): MATH 27700 or equivalent Live. They allow us to prove properties of our programs, thereby guaranteeing that our code is free of software errors. 100 Units. CMSC15100-15200. Courses that fall into this category will be marked as such. Generally offered alternate years. Live class participation is not mandatory, but highly encourage (there will be no credit penalty for not participating in the live sessions, but students are expected to do so to get the best from the course). STAT 37750: Compressed Sensing (Foygel-Barber) Spring. Computing Courses - 250 units. 100 Units. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. Download (official online versions from MIT Press): book ( PDF, HTML ). 100 Units. F: less than 50%. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. Email policy: The TAs and I will prioritize answering questions posted to Piazza, NOT individual emails. Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Focuses specifically on deep learning and emphasizes theoretical and intuitive understanding. 100 Units. Unsupervised learning and clustering 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. Students are required to complete both written assignments and programming projects using OpenGL. Random forests, bagging We will study computational linguistics from both scientific and engineering angles: the use of computational modeling to address scientific questions in linguistics and cognitive science, as well as the design of computational systems to solve engineering problems in natural language processing (NLP). Computer science majors must take courses in the major for quality grades. Instructor(s): H. GunawiTerms Offered: Autumn This course aims to introduce computer scientists to the field of bioinformatics. This course covers computational methods for structuring and analyzing data to facilitate decision-making. 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. 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. Curriculum. Tensions often arise between a computer system's utility and its privacy-invasiveness, between its robustness and its flexibility, and between its ability to leverage existing data and existing data's tendency to encode biases. 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. Students may substitute upper-level or graduate courses in similar topics for those on the list that follows with the approval of the departmental counselor. Introduction to Computer Security. Instructor(s): S. KurtzTerms Offered: Spring Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) Instructor(s): Allyson EttingerTerms Offered: Autumn Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. 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. Terms Offered: Autumn 100 Units. 100 Units. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Students may not use AP credit for computer science to meet minor requirements. The course will be taught at an introductory level; no previous experience is expected. Prerequisite(s): CMSC 25300, CMSC 25400, CMSC 25025, or TTIC 31020. 100 Units. Equivalent Course(s): ASTR 21400, ASTR 31400, PSMS 31400, CHEM 21400, PHYS 21400. 1. The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. Two exams (20% each). Students should consult course-info.cs.uchicago.edufor up-to-date information. Faculty-led research groups exploring research areas within computer science and its interdisciplinary applications. Chicago, IL 60637 The University of Chicago Booth School of Business how to fast forward a video on iphone mathematical foundations of machine learning uchicagobest brands to thrift and resellbest brands to thrift and resell Students may petition to take more advanced courses to fulfill this requirement. It also touches on some of the legal, policy, and ethical issues surrounding computer security in areas such as privacy, surveillance, and the disclosure of security vulnerabilities. Equivalent Course(s): MAAD 25300. This course will focus on analyzing complex data sets in the context of biological problems. 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. In this class, we critically examine emergent technologies that might impact the future generations of computing interfaces, these include: physiological I/O (e.g., brain and muscle computer interfaces), tangible computing (giving shape and form to interfaces), wearable computing (I/O devices closer to the user's body), rendering new realities (e.g., virtual and augmented reality), haptics (giving computers the ability to generate touch and forces) and unusual auditory interfaces (e.g., silent speech and microphones as sensors). The course will consist of bi-weekly programming assignments, a midterm examination, and a final. 100 Units. The final grade will be allocated to the different components as follows: Homework: 30%. 100 Units. TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring. This course will present a practical, hands-on approach to the field of bioinformatics. Note(s): This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 27200 and 27400. 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. Becca: Wednesdays 10:30-11:30AM, JCL 257, starting week of Oct. 7. that at most one of CMSC 25500 and TTIC 31230 count CMSC23010. Tensions often arise between a computer system's utility and its privacy-invasiveness, between its robustness and its flexibility, and between its ability to leverage existing data and existing data's tendency to encode biases. First: some people seem to be misunderstanding 'foundations' in the title. Prerequisite(s): (CMSC 12300 or CMSC 15400), or MAtH 16300 or higher, or by consent. The class will rigorously build up the two pillars of modern . Mathematical Logic I. CMSC23700. Equivalent Course(s): MATH 28000. 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+). Students must be admitted to the joint MS program. The course will unpack and re-entangle computational connections and data-driven interactions between people, built space, sensors, structures, devices, and data. In order to make the operations of the computer more transparent, students will study the C programming language, with special attention devoted to bit-level programming, pointers, allocation, file input and output, and memory layout. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. This course emphasizes the C Programming Language, but not in isolation. This course meets the general education requirement in the mathematical sciences. Prerequisite(s): CMSC 27100, CMSC 27130, or CMSC 37110, or MATH 20400 or MATH 20800. Mathematical Logic II. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. 100 Units. Computer Science with Applications II. Winter Basic topics include processes, threads, concurrency, synchronization, memory management, virtual memory, segmentation, paging, caching, process and I/O scheduling, file systems, storage devices. Since it was introduced in 2019, the data science minor has drawn interest from UChicago students across disciplines. Extensive programming required. This course deals with finite element and finite difference methods for second-order elliptic equations (diffusion) and the associated parabolic and hyperbolic equations. Simple type theory, strong normalization. Waitlist: We will not be accepting auditors this quarter due to high demand. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. Equivalent Course(s): LING 28610. 30546. These tools have two main uses. The course will cover algorithms for symmetric-key and public-key encryption, authentication, digital signatures, hash functions, and other primitives. Honors Theory of Algorithms. Introduction to Software Development. Equivalent Course(s): DATA 11800, STAT 11800. Least squares, linear independence and orthogonality The class will also introduce students to basic aspects of the software development lifecycle, with an emphasis on software design. Note(s): anti-requisites: CMSC 25900, DATA 25900. Developing machine learning algorithms is easier than ever. CMSC29700. 100 Units. Note(s): This course meets the general education requirement in the mathematical sciences. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. 100 Units. Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. The PDF will include all information unique to this page. Prerequisite(s): Placement into MATH 13100 or higher, or by consent. It describes several important modern algorithms, provides the theoretical . This is a project oriented course in which students will construct a fully working compiler, using Standard ML as the implementation language. $85.00 Hardcover. If you have any problems or feedback for the developers, email team@piazza.com. - Financial Math at UChicago literally . Students will also gain basic facility with the Linux command-line and version control. It involves deeply understanding various community needs and using this understanding coupled with our knowledge of how people think and behave to design user-facing interfaces that can enhance and augment human capabilities. 100 Units. 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. 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. 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. CMSC27700. Data types include images, archives of scientific articles, online ad clickthrough logs, and public records of the City of Chicago. The mathematical and algorithmic foundations of scientific visualization (for example, scalar, vector, and tensor fields) will be explained in the context of real-world data from scientific and biomedical domains. 100 Units. CMSC14100. 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. Note(s): Students can use at most one of CMSC 25500 and TTIC 31230 towards a CS major or CS minor. 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. Summer B-: 80% or higher 100 Units. 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. Prerequisite(s): CMSC 27100, or MATH 20400 or higher. Prerequisite(s): CMSC 15400. This course is an introduction to the design and analysis of cryptography, including how "security" is defined, how practical cryptographic algorithms work, and how to exploit flaws in cryptography. 100 Units. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200. Learning goals and course objectives. Introduction to Computer Science I. A-: 90% or higher Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. Prerequisite(s): (CMSC 12200 or CMSC 15200 or CMSC 16200) and (CMSC 27200 or CMSC 27230 or CMSC 37000). Mathematical Foundations of Machine Learning. CMSC21400. This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. 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(s): A more detailed course description should be available later. This course is cross-listed between CS, ECE, and . Requires TTIC31020as a prerequisite, and relies on a similar or slightly higher mathematical preparation. 100 Units. Features and models Digital fabrication involves translation of a digital design into a physical object. You will learn about different underserved and marginalized communities such as children, the elderly, those needing assistive technology, and users in developing countries, and their particular needs.

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