Machine Learning: three courses from this list. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. CMSC28510. There is a mixture of individual programming assignments that focus on current lecture material, together with team programming assignments that can be tackled using any Unix technology. AI approaches hold promise for improving models of climate and the universe, transforming waste products into energy sources, detecting new particles at the Large Hadron Collider, and countless . Both courses in this sequence meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15200 or 16200 to meet requirements for the major. CMSC22600. This site uses cookies from Google to deliver its services and to analyze traffic. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself. The textbooks will be supplemented with additional notes and readings. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. We'll explore creating a story, pitching the idea, raising money, hiring, marketing, selling, and more. What is ML, how is it related to other disciplines? A written report is . 100 Units. At what level does an entering student begin studying computer science at the University of Chicago? Topics include automata theory, regular languages, context-free languages, and Turing machines. 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. Introduction to Database Systems. CMSC23310. Machine Learning and Large-Scale Data Analysis. 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. 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 Methods of enumeration, construction, and proof of existence of discrete structures are discussed in conjunction with the basic concepts of probability theory over a finite sample space. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Does human review of algorithm sufficient, and in what cases? A grade of C- or higher must be received in each course counted towards the major. )" Skip to search form Skip to main content Skip to account menu. If you have any problems or feedback for the developers, email team@piazza.com. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. See also some notes on basic matrix-vector manipulations. Note(s): Prior experience with basic linear algebra (matrix algebra) is recommended. The course will also cover special topics such as journaling/transactions, SSD, RAID, virtual machines, and data-center operating systems. 100 Units. Visualizations will be primarily web-based, using D3.js, and possibly other higher-level languages and libraries. Winter Prerequisite(s): By consent of instructor and approval of department counselor. Instructor(s): Y. LiTerms Offered: Autumn But the Introduction to Data Science sequence changed her view. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. Note(s): First year students are not allowed to register for CMSC 12100. Winter 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. Unsupervised learning and clustering Lectures cover topics in (1) data representation, (2) basics of relational databases, (3) shell scripting, (4) data analysis algorithms, such as clustering and decision trees, and (5) data structures, such as hash tables and heaps. Information about your use of this site is shared with Google. Autumn/Spring. Advanced Algorithms. CMSC21400. CMSC23210. The UChicago/Argonne team is well suited to shoulder the multidisciplinary breadth of the project, which spans from mathematical foundations to cutting edge data and computer science concepts in artificial . Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. Students do reading and research in an area of computer science under the guidance of a faculty member. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security 100 Units. Instructor consent required. Request form available online https://masters.cs.uchicago.edu ); internet and routing protocols (IP, IPv6, ARP, etc. This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. Foundations of Machine Learning. 100 Units. Summer C+: 77% or higher Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). Prerequisite(s): (CMSC 12200 or CMSC 15200 or CMSC 16200) and (CMSC 27200 or CMSC 27230 or CMSC 37000). It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Students can earn a BA or BS degree with honors by attaining a grade of B or higher in all courses in the major and a grade of B or higher in three approved graduate computer science courses (30000-level and above). SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. The course is also intended for students outside computer science who are experienced with programming and computing with scientific data. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. Instructor(s): LopesTerms Offered: Spring In total, the Financial Mathematics degree requires the successful completion of 1250 units. Introduction to Computer Science I-II. Students will also gain basic facility with the Linux command-line and version control. B: 83% or higher This course is a direct continuation of CMSC 14100. 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. CMSC28540. The class will rigorously build up the two pillars of modern . To do so, students must choose three of their electives from the relevant approved specialization list. This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. Coursicle helps you plan your class schedule and get into classes. The course will combine analysis and discussion of these approaches with training in the programming and mathematical foundations necessary to put these methods into practice. (Links to an external site.) This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. Boyd, Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares(available onlinehere) Other new courses in development will cover misinterpretation of data, the economic value of data and the mathematical foundations of machine learning and data science. Please sign up for the waitlist (https://waitlist.cs.uchicago.edu/) if you are looking for a spot. 100 Units. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. 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. Programming Proofs. Pattern Recognition and Machine Learning; by Christopher Bishop, 2006. 1. Inventing, Engineering and Understanding Interactive Devices. 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. Equivalent Course(s): MPCS 54233. 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. Part 1 covered by Mathematics for Machine Learning). We concentrate on a few widely used methods in each area covered. STAT 41500-41600: High Dimensional Statistics. The ideal student in this course would have a strong interest in the use of computer modeling as predictive tool in a range of discplines -- for example risk management, optimized engineering design, safety analysis, etc. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction Creative Coding. 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. Prerequisite(s): CMSC 15400 Introduction to Data Science II. 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. Decision trees CMSC22001. CMSC21010. UChicago Harris Campus Visit. This course covers principles of modern compiler design and implementation. 1427 East 60th Street CMSC25440. CMSC20600. 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. Placement into MATH 15100 or completion of MATH 13100. Introduction to Formal Languages. The course is designed to accommodate students both with and without prior programming experience. 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). Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. There are three different paths to a, Digital Studies of Language, Culture, and History, History, Philosophy, and Social Studies of Science and Medicine, General Education Sequences for Science Majors, Elementary Functions and Calculus I-II (or higher), Engineering Interactive Electronics onto Printed Circuit Boards. To do so, students must take three courses from an approved list in lieu of three major electives. Digital fabrication involves translation of a digital design into a physical object. Prerequisite(s): (CMSC 12300 or CMSC 15400), or MAtH 16300 or higher, or by consent. Note(s): This course meets the general education requirement in the mathematical sciences. Students will gain basic fluency with debugging tools such as gdb and valgrind and build systems such as make. Reading and Research in Computer Science. Course #. 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). CMSC22000. Terms Offered: Autumn 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. Equivalent Course(s): CMSC 33250. This course will introduce fundamental concepts in natural language processing (NLP). 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