This course emphasizes the C Programming Language, but not in isolation. At UChicago CS, we welcome students of all backgrounds and identities. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. Mathematical Logic I-II. When she arrived at the University of Chicago, she was passionate about investigative journalism and behavioral economics, with a focus on narratives over number-crunching. Developing machine learning algorithms is easier than ever. Prerequisite(s): CMSC 20300 The course is also intended for students outside computer science who are experienced with programming and computing with scientific data. We will have several 3D printers available for use during the class and students will design and fabricate several parts during the course. Techniques studied include the probabilistic method. Equivalent Course(s): MPCS 51250. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. Application: electronic health record analysis, Professor of Statistics and Computer Science, University of Chicago, Auto-differentiable Ensemble Kalman Filters, Pure exploration in kernel and neural bandits, Mathematical Foundations of Machine Learning (Fall 2021), https://piazza.com/uchicago/fall2019/cmsc2530035300stat27700/home, https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. A-: 90% or higher This exam will be offered in the summer prior to matriculation. Note: Students may petition to have graduate courses count towards their specialization. Terms 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). Quizzes: 30%. 100 Units. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. 100 Units. 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. Organizations from academia, industry, government, and the non-profit sector that collaborate with UChicago CS. Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 Machine Learning and Large-Scale Data Analysis. Prerequisite(s): First year students are not allowed to register for CMSC 12100. 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. D: 50% or higher 100 Units. Develops data-driven systems that derive insights from network traffic and explores how network traffic can reveal insights into human behavior. Non-MPCS students must receive approval from program prior to registering. 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 Robotics. B-: 80% or higher Non-majors may take courses either for quality grades or, subject to College regulations and with consent of the instructor, for P/F grading. A computer graphics collective at UChicago pursuing innovation at the intersection of 3D and Deep Learning. 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. Note(s): This course meets the general education requirement in the mathematical sciences. Instructor(s): B. SotomayorTerms Offered: Spring This course will focus on analyzing complex data sets in the context of biological problems. Introduction to Data Science II. Prerequisite(s): Placement into MATH 13100 or higher, or by consent. More than half of the requirements for the minor must be met by registering for courses bearing University of Chicago course numbers. Prerequisite(s): CMSC 15400 or CMSC 22000. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. 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. High-throughput automated biological experiments require advanced algorithms, implemented in high-performance computing systems, to interpret their results. Instructor(s): Staff Introduction to Quantum Computing. Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). 100 Units. Terms Offered: Spring 100 Units. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. 2017 The University of Chicago mathematical foundations of machine learning uchicago. Director, Machine Learning Engineer Bain & Company Frankfurt, Hesse, Germany 5 days ago Be among the first 25 applicants Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. 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. Prerequisite(s): CMSC 15400 The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. These tools have two main uses. Instructor(s): A. ChienTerms Offered: Winter This is a rigorous mathematical course providing an analytic view of machine learning. To better appreciate the challenges of recent developments in the field of Distributed Systems, this course will guide students through seminal work in Distributed Systems from the 1970s, '80s, and '90s, leading up to a discussion of recent work in the field. Sec 02: MW 9:00 AM-10:20AM in Crerar Library 011, Textbook(s): Eldn,Matrix Methods in Data Mining and Pattern Recognition(recommended). Prerequisite(s): CMSC 15400 and some experience with 3D modeling concepts. Mathematical Foundations of Machine Learning. Introduction to Complexity Theory. Students may not use AP credit for computer science to meet minor requirements. Equivalent Course(s): MATH 27700. CMSC13600. Terms Offered: Autumn,Spring,Summer,Winter 100 Units. ); end-to-end protocols (UDP, TCP); and other commonly used network protocols and techniques. Machine Learning and Algorithms | Financial Mathematics | The University of Chicago Home / Curriculum / Machine Learning and Algorithms Machine Learning and Algorithms 100 Units Needed for Degree Completion Any Machine Learning and Algorithms Courses taken in excess of 100 units count towards the Elective requirement. Becca: Wednesdays 10:30-11:30AM, JCL 257, starting week of Oct. 7. UChicago (9) iversity (9) SAS Institute (9) . Students must be admitted to the joint MS program. Advanced Database Systems. Note(s): This course is offered in alternate years. 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. 100 Units. CMSC23310. Functional Programming. This course takes a technical approach to understanding ethical issues in the design and implementation of computer systems. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. Undergraduate Computational Linguistics. In this course, we will enrich our perspective about these two related but distinct mechanisms, by studying the statically-typed pure functional programming language Haskell. 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. The course is open to undergraduates in all majors (subject to the pre-requisites), as well as Master's and Ph.D. students. Decision trees 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. Students will be able to choose from multiple tracks within the data science major, including a theoretical track, a computational track and a general track balanced between the . Students can select data science as their primary program of study, or combine the interdisciplinary field with a second major. CMSC28130. Least squares, linear independence and orthogonality STAT 34000: Gaussian Processes (Stein) Spring. CMSC25460. Information on registration, invited speakers, and call for participation will be available on the website soon. Computer Architecture for Scientists. These include linear and logistic regression and . 100 Units. Autumn/Spring. The textbooks will be supplemented with additional notes and readings. 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. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. We will closely read Shoshana Zuboff's Surveillance Capitalism on tour through the sociotechnical world of AI, alongside scholarship in law, philosophy, and computer science to breathe a human rights approach to algorithmic life. Recently, The High Commissioner for Human Rights called for states to place moratoriums on AI until it is compliant with human rights. Introduction to Computer Systems. Prerequisite(s): CMSC 15400 and (CMSC 27100 or CMSC 27130 or CMSC 37110). Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Most of the skills required for this process have nothing to do with one's technical capacity. Terms Offered: Winter 100 Units. Linear algebra strongly recommended; a 200-level Statistics course recommended. Final: TBD. Matlab, Python, Julia, or R). Click the Bookmarks tab when you're watching a session; 2. Students will be expected to actively participate in team projects in this course. . 100 Units. Engineering for Ethics, Privacy, and Fairness in Computer Systems. CMSC23200. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Prerequisite(s): CMSC 15200 or CMSC 16200. The graduate versions of Discrete Mathematics and/or Theory of Algorithms can be substituted for their undergraduate counterparts. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Terms Offered: Winter Waitlist: We will not be accepting auditors this quarter due to high demand. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. 5747 South Ellis Avenue Instructor(s): Feamster, NicholasTerms Offered: Winter 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. CMSC27502. The College and the Department of Computer Science offer two placement exams to help determine the correct starting point: The Online Introduction to Computer Science Exam may be taken (once) by entering students or by students who entered the College prior to Summer Quarter 2022. Midterm: Wednesday, Oct. 30, 6-8pm, location TBD Students will complete weekly problem sets, as well as conduct novel research in a group capstone project. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. Computability topics are discussed (e.g., the s-m-n theorem and the recursion theorem, resource-bounded computation). Its really inspiring that I can take part in a field thats rapidly evolving.. Random forests, bagging CMSC16100. Defining this emerging field by advancing foundations and applications. While this course is not a survey of different programming languages, we do examine the design decisions embodied by various popular languages in light of their underlying formal systems. The computer science minor must include three courses chosen from among all 20000-level CMSC courses and above. Instructor(s): Stuart KurtzTerms Offered: TBD Note: students who earned a Pass or quality grade of D or better in CMSC 13600 may not enroll in CMSC 21800. Students may also earn a BA or BS degree with honors by attaining the same minimum B grade in all courses in the major and by writing a successful bachelor's thesis as part of CMSC29900 Bachelor's Thesis. Final: Wednesday, March 13, 6-8pm in KPTC 120. 100 Units. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. 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 only opportunity students will have to complete the retired introductory sequence is as follows: Students who are not able to complete the retired introductory sequence on this schedule should contact the Director of Undergraduate Studies for Computer Science or the Computer Science Major Adviser for guidance. David Biron, director of undergraduate studies for data science, anticipates that many will choose to double major in data science and another field. This course covers principles of modern compiler design and implementation. Ethics, Fairness, Responsibility, and Privacy in Data Science. Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. CMSC28100. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Through the new undergraduate major in data science available in the 2021-22 academic year, University of Chicago College students will learn how to analyze data and apply it to critical real-world problems in medicine, public policy, the social and physical sciences, and many other domains. Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. Through the new Data Science Clinic, students will capstone their studies by working with government, non-profit and industry partners on projects using data science approaches in real world situations with immediate, substantial impact. Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. CMSC23300. 100 Units. How can we determine the order of events in a system where we can't assume a single global clock? We also discuss the Gdel completeness theorem, the compactness theorem, and applications of compactness to algebraic problems. Instructor(s): ChongTerms Offered: Spring (Links to an external site. The Core introduces students to a world of general knowledge useful for the active, but highly thoughtful practice of modern citizenship, while our brilliant majors enable students to gain active experience in the excitement of fundamental, pathbreaking research. Though its origins are ancient, cryptography now underlies everyday technologies including the Internet, wifi, cell phones, payment systems, and more. 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. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. You can read more about Prof. Rigollet's work and courses [on his . 100 Units. Programming Languages. Inclusive Technology: Designing for Underserved and Marginalized Populations. 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. 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. Scientific Visualization. 100 Units. The course is designed to accommodate students both with and without prior programming experience. Computing Courses - 250 units. There are three different paths to a Bx/MS: a research-oriented program for computer science majors, a professionally oriented program for computer science majors, and a professionally oriented program for non-majors. Prerequisite(s): CMSC 14300, or placement into CMSC 14400, is a prerequisite for taking this course. 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. Students who major in computer science have the option to complete one specialization. Students will gain further fluency with debugging tools and build systems. This course covers the basics of computer systems from a programmer's perspective. 30546. Lecture 1: Intro -- Mathematical Foundations of Machine Learning 100 Units. All rights reserved. 100 Units. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. CMSC22880. Tue., January 17, 2023 | 10:30 AM. The class will rigorously build up the two pillars of modern . The textbooks will be supplemented with additional notes and readings. Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets Quizzes will be via canvas and cover material from the past few lectures. Matlab, Python, Julia, R). This course will explore the design, optimization, and verification of the software and hardware involved in practical quantum computer systems. Please retrieve the Zoom meeting links on Canvas. Quizzes (10%): Quizzes will be via canvas and cover material from the past few lectures. Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. CMSC22000. Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT. relationship between worldmaking and technology through social, political, and technical lenses. Unsupervised learning and clustering 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. Advanced Distributed Systems. 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. We concentrate on a few widely used methods in each area covered. 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. Plan accordingly. Two new projects will test out ways to make "intelligent" water [] 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. This course covers education theory, psychology (e.g., motivation, engagement), and game design so that students can design and build an educational learning application. Church's -calculus, -reduction, the Church-Rosser theorem. Dependent types. CMSC27700. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction 100 Units. Data Visualization. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Honors Introduction to Computer Science II. 100 Units. Winter Quarter Prerequisite(s): CMSC 15400 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. CMSC28540. Type a description and hit enter to create a bookmark; 3. Instructor(s): Y. LiTerms Offered: Autumn This course focuses on the principles and techniques used in the development of networked and distributed software. Machine learning algorithms are also used in data modeling.