Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. CMSC23310. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. BS students also take three courses in an approved related field outside computer science. While this course should be of interest for students interested in biological sciences and biotechnology, techniques and approaches taught will be applicable to other fields. 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. Security, Privacy, and Consumer Protection. Final: Wednesday, March 13, 6-8pm in KPTC 120. Equivalent Course(s): MAAD 13450, HMRT 23450. Plan accordingly. Prerequisite(s): CMSC 15400 or CMSC 12200 and STAT 22000 or STAT 23400, or by consent. Equivalent Course(s): CAPP 30350, CMSC 30350. CMSC21800. Through both computer science and studio art, students will design algorithms, implement systems, and create interactive artworks that communicate, provoke, and reframe pervasive issues in modern privacy and security. We cover various standard data structures, both abstractly, and in terms of concrete implementations-primarily in C, but also from time to time in other contexts like scheme and ksh. CMSC20300. CMSC23710. We will cover algorithms for transforming and matching data; hypothesis testing and statistical validation; and bias and error in real-world datasets. 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/. 100 Units. We will use traditional machine learning methods as well as deep learning depending on the problem. Courses that fall into this category will be marked as such. Programming languages often conflate the definition of mathematical functions, which deterministically map inputs to outputs, and computations that effect changes, such as interacting with users and their machines. Please be aware that course information is subject to change, and the catalog does not necessarily reflect the most recent information. As such it has been a fertile ground for new statistical and algorithmic developments. CMSC14400. 100 Units. 100 Units. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. Faculty-led research groups exploring research areas within computer science and its interdisciplinary applications. Now supporting the University of Chicago. CMSC11900. Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source . Honors Graph Theory. Becca: Wednesdays 10:30-11:30AM, JCL 257, starting week of Oct. 7. Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). Introduction to Software Development. In recent years, large distributed systems have taken a prominent role not just in scientific inquiry, but also in our daily lives. F: less than 50%. This can lead to severe trustworthiness issues in ML. Instructor(s): A. RazborovTerms Offered: Autumn CMSC22000. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. Prerequisite(s): CMSC 15400 or CMSC 22000. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. 100 Units. Linear algebra strongly recommended; a 200-level Statistics course recommended. It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. Introduction to Creative Coding. CMSC16200. Note Equivalent Course(s): CMSC 27700, Terms Offered: Autumn Students will continue to use Python, and will also learn C and distributed computing tools and platforms, including Amazon AWS and Hadoop. 3. The article is an analysis of the current topic - digitalization of the educational process. CMSC27700-27800. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. Students may enroll in CMSC29700 Reading and Research in Computer Science and CMSC29900 Bachelor's Thesis for multiple quarters, but only one of each may be counted as a major elective. The final grade will be allocated to the different components as follows: Homework: 30%. The iterative nature of the design process will require an appreciable amount of time outside of class for completing projects. For up-to-date information on our course offerings, please consult course-info.cs.uchicago.edu. This course also includes hands-on labs, where students will enhance their learning by implementing a modern microprocessor in a C simulator. CMSC28515. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. (Links to an external site.) mathematical foundations of machine learning uchicago. 100 Units. The new paradigm of computing, harnessing quantum physics. F: less than 50%. CMSC23400. Students who earn the BA are prepared either for graduate study in computer science or a career in industry. But for data science, experiential learning is fundamental. The course also emphasizes the importance of collaboration in real-world software development, including interpersonal collaboration and team management. Winter We split the book into two parts: Mathematical foundations; Example machine learning algorithms that use the mathematical foundations 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 . 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. Students are expected to have taken calculus and have exposureto numerical computing (e.g. Students are required to complete both written assignments and programming projects using OpenGL. In this class we will engineer electronics onto Printed Circuit Boards (PCBs). D: 50% or higher The core theme for the Entrepreneurship in Technology course is that computer science students need exposure to the broad challenges of capturing opportunities and creating companies. Reviewer 1 Report. CMSC15400. 100 Units. 100 Units. 100 Units. 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. In these opportunities, Kielb utilized her data science toolkit to analyze philanthropic dollars raised for a multi-million dollar relief fund; evaluate how museum members of different ages respond to virtual programming; and generate market insights for a product in its development phase. Students will also gain further fluency in working with the Linux command-line, including some basic operating system concepts, as well as the use of version control systems for collaborative software development. C: 60% or higher Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Instructor(s): Staff Introduction to Scientific Computing. 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. UChicago students will have a wide variety of opportunities to engage projects across different sectors, disciplines and domains, from problems drawn from environmental and human rights groups to AI-driven finance and industry to cutting-edge research problems from the university, our national labs and beyond. Furthermore, the course will examine how memory is organized and structured in a modern machine. Students will program in Python and do a quarter-long programming project. 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. Gaussian mixture models and Expectation Maximization 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. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. Topics include propositional and predicate logic and the syntactic notion of proof versus the semantic notion of truth (e.g., soundness, completeness). 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] Students may petition to have graduate courses count towards their specialization via this same page. Creative Coding. Instructor(s): Autumn Quarter Instructor: Scott WakelyTerms Offered: Autumn This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. Scalable systems are needed to collect, stream, process, and validate data at scale. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. They allow us to prove properties of our programs, thereby guaranteeing that our code is free of software errors. CMSC10450. Equivalent Course(s): CMSC 33210. We'll explore creating a story, pitching the idea, raising money, hiring, marketing, selling, and more. Equivalent Course(s): MATH 28410. Introduction to Computer Vision. Introduction to Computer Security. This exam will be offered in the summer prior to matriculation. This course aims to introduce computer scientists to the field of bioinformatics. Topics covered include two parts: (1) a gentle introduction of machine learning: generalization and model selection, regression and classification, kernels, neural networks, clustering and dimensionality reduction; (2) a statistical perspective of machine learning, where we will dive into several probabilistic supervised and unsupervised models, including logistic regression, Gaussian mixture models, and generative adversarial networks. 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. 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 two. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Prerequisite(s): CMSC 22880 Microsoft. No prior experience in security, privacy, or HCI is required. CMSC23530. 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. Programming will be based on Python and R, but previous exposure to these languages is not assumed. In addition, you will learn how to be mindful of working with populations that can easily be exploited and how to think creatively of inclusive technology solutions. Introduction to Numerical Partial Differential Equations. Programming projects will be in C and C++. Programming Languages. Students will also be introduced to the basics of programming in Python including designing and calling functions, designing and using classes and objects, writing recursive functions, and building and traversing recursive data structures. CMSC22200. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. Spring CMSC23206. Digital fabrication involves translation of a digital design into a physical object. Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000) and CMSC 25300. The course will involve a substantial programming project implementing a parallel computations. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Further topics include proof by induction; recurrences and Fibonacci numbers; graph theory and trees; number theory, congruences, and Fermat's little theorem; counting, factorials, and binomial coefficients; combinatorial probability; random variables, expected value, and variance; and limits of sequences, asymptotic equality, and rates of growth. 100 Units. Foundations of Machine Learning. (0) 2022.11.13: Computer Vision: (0) 2022.11.13: Machine Learning with Python - Clustering (0) 2022.10.07 Prerequisite(s): CMSC 15400 or CMSC 22000 CMSC 23206 Security, Privacy, and Consumer Protection, CMSC 25910 Engineering for Ethics, Privacy, and Fairness in Computer Systems, Bachelor's thesis in computer security, approved as such, CMSC 22240 Computer Architecture for Scientists, CMSC 23300 Networks and Distributed Systems, CMSC 23320 Foundations of Computer Networks, CMSC 23500 Introduction to Database Systems, CMSC 25422 Machine Learning for Computer Systems, Bachelor's thesis in computer systems, approved as such, CMSC 25025 Machine Learning and Large-Scale Data Analysis, CMSC 25300 Mathematical Foundations of Machine Learning, Bachelor's thesis in data science, approved as such, CMSC 20370 Inclusive Technology: Designing for Underserved and Marginalized Populations, CMSC 20380 Actuated User Interfaces and Technology, CMSC 23220 Inventing, Engineering and Understanding Interactive Devices, CMSC 23230 Engineering Interactive Electronics onto Printed Circuit Boards, CMSC 23240 Emergent Interface Technologies, CMSC 30370 Inclusive Technology: Designing for Underserved and Marginalized Populations, Bachelor's thesis in human computer interaction, approved as such, CMSC 25040 Introduction to Computer Vision, CMSC 25500 Introduction to Neural Networks, TTIC 31020 Introduction to Machine Learning, TTIC 31120 Statistical and Computational Learning Theory, TTIC 31180 Probabilistic Graphical Models, TTIC 31210 Advanced Natural Language Processing, TTIC 31220 Unsupervised Learning and Data Analysis, TTIC 31250 Introduction to the Theory of Machine Learning, Bachelor's thesis in machine learning, approved as such, CMSC 22600 Compilers for Computer Languages, Bachelor's thesis in programming languages, approved as such, CMSC 28000 Introduction to Formal Languages, CMSC 28100 Introduction to Complexity Theory, CMSC 28130 Honors Introduction to Complexity Theory, Bachelor's thesis in theory, approved as such. An understanding of the techniques, tricks, and traps of building creative machines and innovative instrumentation is essential for a range of fields from the physical sciences to the arts. Advanced Database Systems. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. Students with no prior experience in computer science should plan to start the sequence at the beginning in, Students who are interested in data science should consider starting with, The Online Introduction to Computer Science Exam. Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) Now shes using her data science knowledge in a summer internship analyzing health care technology investment opportunities. Note(s): This course meets the general education requirement in the mathematical sciences. Students can select data science as their primary program of study, or combine the interdisciplinary field with a second major. 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. CMSC27620. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. Features and models 1. While digital fabrication has been around for decades, only now has it become possible for individuals to take advantage of this technology through low cost 3D printers and open source tools for 3D design and modeling. Thanks to the fantastic effort of many talented developers, these are easy to use and require only a superficial familiarity . 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. About this Course. In this course we will cover the foundations of 3D object design including computational geometry, the type of models that can and can't be fabricated, the uses and applications of digital fabrication, the algorithms, methods and tools for conversion of 3D models to representations that can be directly manufactured using computer controlled machines, the concepts and technology used in additive manufacturing (aka 3D printing) and the research and practical challenges of developing self-replicating machines. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the hold zoom meetings, where you can participate, ask questions directly to the instructor. Note(s): This course is offered in alternate years. Proficiency in Python is expected. All rights reserved. Instructor(s): K. Mulmuley Instructor(s): ChongTerms Offered: Spring Computing systems have advanced rapidly and transformed every aspect of our lives for the last few decades, and innovations in computer architecture is a key enabler. 100 Units. Networks help explain phenomena in such technological, social, and biological domains as the spread of opinions, knowledge, and infectious diseases. Note: Students may petition to have graduate courses count towards their specialization. Non-majors may take courses either for quality grades or, subject to College regulations and with consent of the instructor, for P/F grading. 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