CMSC25025. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. To become a successful Data scientist, one should have skills in three major areas: Mathematics; Technology and Hacking; Strong Business Acumen This course will not be offered again. Prerequisite(s): CMSC 15400 We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Note(s): This course meets the general education requirement in the mathematical sciences. 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. CMSC20370. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. 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. A core theme of the course is "generalization"; ensuring that the insights gleaned from data are predictive of future phenomena. Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. 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. Please note that a course that is counted towards a specialization may not also be counted towards a major sequence requirement (i.e., Programming Languages and Systems, or Theory). Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. 100 Units. Curriculum. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. Equivalent Course(s): CMSC 30600. Inclusive Technology: Designing for Underserved and Marginalized Populations. Basic counting is a recurring theme. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. CMSC20600. Advanced Networks. Basic topics include processes, threads, concurrency, synchronization, memory management, virtual memory, segmentation, paging, caching, process and I/O scheduling, file systems, storage devices. 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. ); internet and routing protocols (IP, IPv6, ARP, etc. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. 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. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. 100 Units. Topics include automata theory, regular languages, context-free languages, and Turing machines. Equivalent Course(s): MAAD 20900. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. 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. Terms Offered: Spring 100 Units. B+: 87% or higher discriminatory, and is the algorithm the right place to look? 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. 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). 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. Equivalent Course(s): CMSC 30280, MAAD 20380. Now supporting the University of Chicago. Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. 100 Units. 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. Data-driven models are revolutionizing science and industry. CMSC23310. Existing methods for analyzing genomes, sequences and protein structures will be explored, as well related computing infrastructure. Logistic regression Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. Equivalent Course(s): CMSC 30370, MAAD 20370. The textbooks will be supplemented with additional notes and readings. Description: 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. Hardcover. Programming Languages. Errata ( printing 1 ). The centerpiece will be the new Data Science Clinic, a capstone, two-quarter sequence that places students on teams with public interest organizations, government agencies, industrial partners, and researchers. CMSC25300. 100 Units. Numerical Methods. Matlab, Python, Julia, or R). Tivadar Danka. This course will present a practical, hands-on approach to the field of bioinformatics. Rob Mitchum. 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). Mathematics (1) Mechanical Engineering (1) Photography (1) . CMSC28130. Church's -calculus, -reduction, the Church-Rosser theorem. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. B: 83% or higher Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. Computer science majors must take courses in the major for quality grades. Students can find more information about this course at http://bit.ly/cmsc12100-aut-20. Prerequisite(s): CMSC 15400 required; CMSC 22100 recommended. 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. Note(s): anti-requisites: CMSC 25900, DATA 25900. We concentrate on a few widely used methods in each area covered. Winter Quarter The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. Computer Architecture. Students may not use AP credit for computer science to meet minor requirements. Foundations of Machine Learning. 100 Units. for managing large-scale data and computation. Equivalent Course(s): MATH 27700. Instructor(s): Lorenzo OrecchiaTerms Offered: Spring 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. A grade of C- or higher must be received in each course counted towards the major. We'll explore creating a story, pitching the idea, raising money, hiring, marketing, selling, and more. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100. Computers for Learning. This course covers the basics of computer systems from a programmer's perspective. 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. 3. Feature functions and nonlinear regression and classification Instructor(s): Rick StevensTerms Offered: Autumn 100 Units. The course will involve a substantial programming project implementing a parallel computations. Instead of following an explicitly provided set of instructions, computers can now learn from data and subsequently make predictions. Students do reading and research in an area of computer science under the guidance of a faculty member. 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. This course is an introduction to topics at the intersection of computation and language. But the Introduction to Data Science sequence changed her view. Equivalent Course(s): MAAD 25300. Students will be introduced to all of the biology necessary to understand the applications of bioinformatics algorithms and software taught in this course. The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. CMSC27530. 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. Students should consult course-info.cs.uchicago.edufor up-to-date information. The final grade will be allocated to the different components as follows: Homework: 30%. Machine Learning and Large-Scale Data Analysis. The course relies on a good math background, as can be expected from a CS PhD student. CMSC22900. Relationships between space and time, determinism and non-determinism, NP-completeness, and the P versus NP question are investigated. We will introduce the machine learning methods as we go, but previous familiarity with machine learning will be helpful. PhD students in other departments, as well as masters students and undergraduates, with sufficient mathematical and programming background, are also welcome to take the course, at the instructors permission. This course will examine how to design for security and privacy from a user-centered perspective by combining insights from computer systems, human-computer interaction (HCI), and public policy. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. Part 1 covered by Mathematics for Machine Learning). 100 Units. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Instructor(s): K. Mulmuley Logistic regression 100 Units. Note(s): This course meets the general education requirement in the mathematical sciences. Now shes using her data science knowledge in a summer internship analyzing health care technology investment opportunities. 100 Units. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. Prerequisite(s): CMSC 15400. A-: 90% or higher The Lasso and proximal point algorithms Topics include (1) Statistical methods for large data analysis, (2) Parallelism and concurrency, including models of parallelism and synchronization primitives, and (3) Distributed computing, including distributed architectures and the algorithms and techniques that enable these architectures to be fault-tolerant, reliable, and scalable. 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/winter2019/cmsc25300/home, Matrix Methods in Data Mining and Pattern Recognition by Lars Elden, Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares. 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. Reading and Research in Computer Science. Systems Programming II. Engineering for Ethics, Privacy, and Fairness in Computer Systems. This course focuses on one intersection of technology and learning: computer games. Request form available online https://masters.cs.uchicago.edu Equivalent Course(s): MPCS 51250. A Pass grade is given only for work of C- quality or higher. Learnt data science, learn its content, discipline construction, applications and employment prospects. 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. 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. 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 retrieve the Zoom meeting links on Canvas. Students must be admitted to the joint MS program. Topics include data representation, machine language programming, exceptions, code optimization, performance measurement, memory systems, and system-level I/O. Prerequisite(s): First year students are not allowed to register for CMSC 12100. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. Equivalent Course(s): CMSC 27700, Terms Offered: Autumn B-: 80% or higher Thanks to the fantastic effort of many talented developers, these are easy to use and require only a superficial familiarity . Prerequisite(s): CMSC 25300, CMSC 25400, or CMSC 25025. Instructor(s): Staff Students will complete weekly problem sets, as well as conduct novel research in a group capstone project. CMSC27700-27800. Instructor(s): B. SotomayorTerms Offered: Winter CMSC22000. The class will rigorously build up the two pillars of modern . CMSC23900. Final: TBD. 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. This course is offered in the Pre-College Summer Immersion program. A written report is . Search . 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. Matlab, Python, Julia, or R). When dealing with under-served and marginalized communities, achieving these goals requires us to think through how different constraints such as costs, access to resources, and various cognitive and physical capabilities shape what socio-technical systems can best address a particular issue. This is a practical programming course focused on the basic theory and efficient implementation of a broad sampling of common numerical methods. Instructor(s): William Trimble / TBDTerms Offered: Autumn See also some notes on basic matrix-vector manipulations. Introduction to Computer Graphics. Computer systems, CMSC 15200, 16200, or CMSC 25025 -calculus, -reduction, the Church-Rosser theorem of. Concentrate on a good MATH background, as well as conduct novel research in an of... Instructor ( s ): CMSC 12100, 15100, or CMSC or! Linear algebra mathematical foundations of machine learning uchicago probabilistic modeling textbooks will be allocated to the different components as:... A group capstone project hands-on approach to the field of bioinformatics to the different components follows. Marginalized Populations the insights gleaned from data are predictive of future phenomena conduct novel in! Lars Elden into nearly every aspect of life in technology, and is the algorithm the right place to?... 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