Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. 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. Now supporting the University of Chicago. Equivalent Course(s): MAAD 25300. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. 100 Units. CMSC27620. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. Note(s): This course meets the general education requirement in the mathematical sciences. Terms Offered: Winter Topics include shortest paths, spanning trees, counting techniques, matchings, Hamiltonian cycles, chromatic number, extremal graph theory, Turan's theorem, planarity, Menger's theorem, the max-flow/min-cut theorem, Ramsey theory, directed graphs, strongly connected components, directed acyclic graphs, and tournaments. She joined the CSU faculty in 2013 after obtaining dual B.S. Prerequisite(s): CMSC 15400 and one of the following: CMSC 22200, CMSC 22240, CMSC 23000, CMSC 23300, CMSC 23320; or by consent. 100 Units. A Pass grade is given only for work of C- quality or higher. Note(s): Prerequisites: CMSC 15400 or equivalent, or graduate student. Instructor(s): Rick StevensTerms Offered: Autumn BS students also take three courses in an approved related field outside computer science. Cryptography is the use of algorithms to protect information from adversaries. Terms Offered: Spring Least squares, linear independence and orthogonality The honors version of Theory of Algorithms covers topics at a deeper level. Probabilistic Machine Learning: An Introduction; by Kevin Patrick Murphy, MIT Press, 2021. About this Course. This field is for validation purposes and should be left unchanged. 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. 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. 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. 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. The course relies on a good math background, as can be expected from a CS PhD student. Matlab, Python, Julia, or R). 2017 The University of Chicago 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. Data-driven models are revolutionizing science and industry. CMSC25910. Based on this exam, students may place into: Both the BA and BS in computer science require fulfillment of the general education requirement in the mathematical sciences by completing an approved two-quarter calculus sequence. Students can select data science as their primary program of study, or combine the interdisciplinary field with a second major. Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 - Bayesian Inference and Machine Learning I and II from Gordon Ritter. Machine Learning for Finance . Collaboration both within and across teams will be essential to the success of the project. Gaussian mixture models and Expectation Maximization Through multiple project-based assignments, students practice the acquired techniques to build interactive tangible experiences of their own. 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. 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. 100 Units. 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 takes a technical approach to understanding ethical issues in the design and implementation of computer systems. 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 . Prerequisite(s): By consent of instructor and approval of department counselor. Discrete Mathematics. 100 Units. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. Introduction to Computer Graphics. 100 Units. 100 Units. The use of physical robots and real-world environments is essential in order for students to 1) see the result of their programs 'come to life' in a physical environment and 2) gain experience facing and overcoming the challenges of programming robots (e.g., sensor noise, edge cases due to environment variability, physical constraints of the robot and environment). Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. Natural Language Processing. Prerequisite(s): CMSC 23500. Note(s): Open both to students who are majoring in Computer Science and to nonmajors. 100 Units. CMSC27200. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Proficiency in Python is expected. Students who place into CMSC14300 Systems Programming I will receive credit for CMSC14100 Introduction to Computer Science I and CMSC14200 Introduction to Computer Science II upon passing CMSC14300 Systems Programming I. Equivalent Course(s): MATH 27800. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the 100 Units. Students do reading and research in an area of computer science under the guidance of a faculty member. Generally offered alternate years. Terms Offered: Autumn,Spring,Summer,Winter Introduction to Creative Coding. Non-MPCS students must receive approval from program prior to registering. A grade of C- or higher must be received in each course counted towards the major. files that use the command-line version of DrScheme. 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. Instructor(s): William L Trimble / TBDTerms Offered: Spring This course focuses on the principles and techniques used in the development of networked and distributed software. Mathematical Foundations. CMSC23230. Existing methods for analyzing genomes, sequences and protein structures will be explored, as well related computing infrastructure. Instead, we aim to provide the necessary mathematical skills to read those other books. Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. Note: students who earned a Pass or quality grade of D or better in CMSC 13600 may not enroll in CMSC 21800. 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. Artificial intelligence is a valuable lab assistant, diving deep into scientific literature and data to suggest new experiments, measurements, and methods while supercharging analysis and discovery. Data Analytics. Boolean type theory allows much of the content of mathematical maturity to be formally stated and proved as theorems about mathematics in general. Machine Learning for Computer Systems. Application: text classification, AdaBoost Instructor(s): Austin Clyde, Pozen Center for Human Rights Graduate LecturerTerms Offered: Autumn Matlab, Python, Julia, R). 100 Units. Knowledge of Java required. 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. Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. This course covers the basics of computer systems from a programmer's perspective. 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. 5801 S. Ellis Ave., Suite 120, Chicago, IL 60637, The Day Tomorrow Began series explores breakthroughs at the University of Chicago, Institute of Politics to celebrate 10-year anniversary with event featuring Secretary Antony Blinken, UChicago librarian looks to future with eye on digital and traditional resources, Six members of UChicago community to receive 2023 Diversity Leadership Awards, Scientists create living smartwatch powered by slime mold, Chicago Booths 2023 Economic Outlook to focus on the global economy, Prof. Ian Foster on laying the groundwork for cloud computing, Maroons make history: UChicago mens soccer team wins first NCAA championship, Class immerses students in monochromatic art exhibition, Piece of earliest known Black-produced film found hiding in plain sight, I think its important for young girls to see women in leadership roles., Reflecting on a historic 2022 at UChicago. CMSC23710. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) In total, the Financial Mathematics degree requires the successful completion of 1250 units. Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets CMSC29700. This course is a direct continuation of CMSC 14100. Broadly speaking, Machine Learning refers to the automated identification of patterns in data. Instructor(s): Michael MaireTerms Offered: Winter We will focus on designing and laying out the circuit and PCB for our own custom-made I/O devices, such as wearable or haptic devices. Honors Combinatorics. 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. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. CMSC23320. Bookmarks will appear here. 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. Relationships between space and time, determinism and non-determinism, NP-completeness, and the P versus NP question are investigated. Search 209,580,570 papers from all fields of science. Information on registration, invited speakers, and call for participation will be available on the website soon. The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), a multi-institutional collaboration of Chicago universities studying the foundations and applications of data science, was expanded and renewed for five years through a $10 million grant from the National Science Foundation. The class will rigorously build up the two pillars of modern . Winter By using this site, you agree to its use of cookies. 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. Notes 01, Introduction I. Vector spaces and linear representations Notes 02, first look at linear representations Notes 03, linear vector spaces Notes 04, norms and inner products Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe, Pattern Recognition and Machine Learning by Christopher Bishop, Mondays and Wednesdays, 9-10:20am in Crerar 011, Mondays and Wednesdays, 3-4:15pm in Ryerson 251. CMSC12100. Professor Ritter is one of the best quants in the industry and he has a very unique and insightful way of approaching problems, these courses are a must. Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Prerequisite(s): CMSC 15200 or CMSC 16200. Students are required to complete both written assignments and programming projects using OpenGL. 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). Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. 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. Students may not use AP credit for computer science to meet minor requirements. Each of these mini projects will involve students programming real, physical robots interacting with the real world. Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. Instructor(s): S. Kurtz (Winter), J. Simon (Autumn)Terms Offered: Autumn Instructor(s): Laszlo BabaiTerms Offered: Spring 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. Researchers at the University of Chicago and partner institutions studying the foundations and applications of machine learning and AI. Note(s): This course is offered in alternate years. Introduction to Cryptography. Note(s): This course is offered in alternate years. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. 100 Units. Terms Offered: Autumn Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. CMSC 25025-1: Machine Learning and Large-Scale Data Analysis (Amit) CMSC 25300-1: Mathematical Foundations of Machine Learning (Jonas) CMSC 25910-1: Engineering for Ethics, Privacy, and Fairness in Computer Systems (Ur) CMSC 27200-1: Theory of Algorithms (Orecchia) [Theory B] CMSC 27200-2: Theory of Algorithms (Orecchia) [Theory B] 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. Computer Architecture. This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Numerical Methods. 100 Units. Software Construction. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Class discussion will also be a key part of the student experience. Do predictive models violate privacy even if they do not use or disclose someone's specific data? Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) 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. This course will present a practical, hands-on approach to the field of bioinformatics. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. Prerequisite(s): CMSC 15400 and (CMSC 27100 or CMSC 27130 or CMSC 37110). Prerequisite(s): (CMSC 15200 or CMSC 16200 or CMSC 12200), or (MATH 15910 or MATH 16300 or higher), or by consent. (Links to an external site. Quantum Computer Systems. 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+). CMSC14100. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. Matlab, Python, Julia, R). Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110, or by consent. Machine Learning in Medicine. CMSC22100. 100 Units. Massive Open Online Courses (MOOCs) were created to bring education to those without access to universities, yet most of the students who succeed in them are those who are already successful in the current educational model. Equivalent Course(s): ASTR 21400, ASTR 31400, PSMS 31400, CHEM 21400, PHYS 21400. More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. This class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer primarily to tabulated data). Equivalent Course(s): CMSC 32900. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Terms Offered: Winter I was interested in the more qualitative side, sifting through really large sums of information to try to tease out an untold narrative or a hidden story, said Hitchings, a rising third-year in the College and the daughter of two engineers. Figure 4.1: An algorithmic framework for online strongly convex programming. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. Jointly with the School of the Art Institute of Chicago (SAIC), this course will examine privacy and security issues at the intersection of the physical and digital worlds. Programming Languages and Systems Sequence (two courses required): Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam must replace it with an additional course from this list, For more information, consult the department counselor. Courses fulfilling general education requirements must be taken for quality grades. Random forests, bagging Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. This course is an introduction to scientific programming language design, whereby design choices are made according to rigorous and well-founded lines of reasoning. 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 is a graduate-level CS course with the main target audience being TTIC PhD students (for which it is required) and other CS, statistics, CAM and math PhD students with an interest in machine learning. These include linear and logistic regression and . How does algorithmic decision-making impact democracy? One of the challenges in biology is understanding how to read primary literature, reviewing articles and understanding what exactly is the data that's being presented, Gendel said. Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 CMSC27100. For instance . During lecture time, we will not do the lectures in the usual format, but instead hold zoom meetings, where you can participate in lab sessions, work with classmates on lab assignments in breakout rooms, and ask questions directly to the instructor.

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