Non-majors may take courses either for quality grades or, subject to College regulations and with consent of the instructor, for P/F grading. 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, Defining this emerging field by advancing foundations and applications. Covering a story? Students from 11 different majors, including all four collegiate divisions, have chosen a data science minor. Machine Learning in Medicine. 100 Units. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. discriminatory, and is the algorithm the right place to look? B: 83% or higher BS students also take three courses in an approved related field outside computer science. 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. Email policy: We will prioritize answering questions posted to Ed Discussion, not individual emails. Ashley Hitchings never thought shed be interested in data science. Techniques studied include the probabilistic method. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. It involves deeply understanding various community needs and using this understanding coupled with our knowledge of how people think and behave to design user-facing interfaces that can enhance and augment human capabilities. The course also emphasizes the importance of collaboration in real-world software development, including interpersonal collaboration and team management. 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. 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. Equivalent Course(s): DATA 25422, DATA 35422, CMSC 35422. Introduction to Computer Science II. Even in roles that aren't data science jobs, per se, I had the skill set and I was able to take on added responsibilities, Hitchings said. CMSC16100-16200. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. This first course of the two would . Honors Theory of Algorithms. 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. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. This course is an introduction to key mathematical concepts at the heart of machine learning. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. Gaussian mixture models and Expectation Maximization This course will explore the design, optimization, and verification of the software and hardware involved in practical quantum computer systems. Kernel methods and support vector machines The Center for Data and Computing is an intellectual hub and incubator for data science and artificial intelligence research at the University of Chicago. Equivalent Course(s): MAAD 20900. 100 Units. Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. 100 Units. It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. Homework problems include both mathematical derivations and proofs as well as more applied problems that involve writing code and working with real or synthetic data sets. 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. Students may substitute upper-level or graduate courses in similar topics for those on the list that follows with the approval of the departmental counselor. STAT 41500-41600: High Dimensional Statistics. 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. 100 Units. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. 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. 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 focuses on advanced concepts of database systems topics and assumes foundational knowledge outlined in CMSC 23500. It requires a high degree of mathematical maturity, typical of mathematically-oriented CS and statistics PhD students or math graduates. Prerequisite(s): CMSC 15400 Honors Introduction to Computer Science I. Current focus areas include new techniques to capture 3d models (depth sensors, stereo vision), drones that enable targeted, adaptive, focused sensing, and new 3d interactive applications (augmented reality, cyberphysical, and virtual reality). The textbooks will be supplemented with additional notes and readings. In this course, students will develop a deeper understanding of what a computer does when executing a program. How do we ensure that all the machines have a consistent view of the system's state? Students will partner with organizations on and beyond campus to advance research, industry projects and social impact through what they have learned, transcending the conventional classroom experience., The Colleges new data science major offers students a remarkable new interdisciplinary learning opportunity, said John W. Boyer, dean of the College. A-: 90% or higher Chicago, IL 60637 Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000) and CMSC 25300. 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. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. Most of the skills required for this process have nothing to do with one's technical capacity. Introduction to Optimization. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Students who major in computer science have the option to complete one specialization. Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. Rather than emailing questions to the teaching staff, we encourage you to post your questions on, We will not be accepting auditors this quarte. The Barendregt cube of type theories. Does human review of algorithm sufficient, and in what cases? 100 Units. CMSC15100-15200. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. 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] We compliment the lectures with weekly programming assignments and two larger projects, in which we build/program/test user-facing interactive systems. CMSC25440. This course is an introduction to programming, using exercises in graphic design and digital art to motivate and employ basic tools of computation (such as variables, conditional logic, and procedural abstraction). Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. By Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. Data science is more than a hot tech buzzword or a fashionable career; in the century to come, it will be an essential toolset in almost any field. 100 Units. This course aims to introduce computer scientists to the field of bioinformatics. They also allow us to formalize mathematics, stating and proving mathematical theorems in a manner that leaves no doubt as to their meaning or veracity. 100 Units. Note(s): First year students are not allowed to register for CMSC 12100. 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. CMSC22100. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. - Financial Math at UChicago literally . 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. 100 Units. This course is an introduction to "big" data engineering where students will receive hands-on experience building and deploying realistic data-intensive systems. Final: Wednesday, March 13, 6-8pm in KPTC 120. 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. Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss CMSC21010. CMSC 35300 Mathematical Foundations of Machine Learning; MACS 33002 Introduction to Machine Learning . Advanced Networks. 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. Students are required to submit the College Reading and Research Course Form. CMSC20900. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. CMSC 29700. Quizzes: 30%. by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. Students may petition to take more advanced courses to fulfill this requirement. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. Computers for Learning. Learning goals and course objectives. Introduction to Software Development. An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. We will introduce core security and privacy technologies, as well as HCI techniques for conducting robust user studies. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. Class discussion will also be a key part of the student experience. No prior experience in security, privacy, or HCI is required. Reflecting the holistic vision for data science at UChicago, data science majors will also take courses in Ethics, Fairness, Responsibility, and Privacy in Data Science and the Societal Impacts of Data, exploring the intensifying issues surrounding the use of big data and analytics in medicine, policy, business and other fields. 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. Rob Mitchum. Techniques studied include the probabilistic method. 100 Units. A range of data types and visual encodings will be presented and evaluated. UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019 UChicago STAT 31140: Computational Imaging Theory and Methods UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019 UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017 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). Instructor(s): LopesTerms Offered: Spring Recent approaches have unlocked new capabilities across an expanse of applications, including computer graphics, computer vision, natural language processing, recommendation engines, speech recognition, and models for understanding complex biological, physical, and computational systems. 100 Units. This exam will be offered in the summer prior to matriculation. 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. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. This concise review of linear algebra summarizes some of the background needed for the course. Note(s): A more detailed course description should be available later. Instructor(s): Ketan MulmuleyTerms Offered: Autumn Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. Features and models CMSC22880. CMSC27200. Prerequisite(s): CMSC 27100, or MATH 20400 or higher. In the course of collecting and interpreting the known data, the authors cite the pedagogical foundations of digital literacy, the current state of digital learning and problems, and the prospects for the development of this direction in the future are also considered. 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. Chapters Available as Individual PDFs Shannon Theory Fourier Transforms Wavelets Tue., January 17, 2023 | 10:30 AM. This course covers the basics of computer systems from a programmer's perspective. 100 Units. Prerequisite(s): CMSC 15400. Equivalent Course(s): MPCS 51250. 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. Foundations of Computer Networks. One central component of the program was formalizing basic questions in developing areas of practice and gaining fundamental insights into these. Other topics include basic counting, linear recurrences, generating functions, Latin squares, finite projective planes, graph theory, Ramsey theory, coloring graphs and set systems, random variables, independence, expected value, standard deviation, and Chebyshev's and Chernoff's inequalities. The course will involve a substantial programming project implementing a parallel computations. Information about your use of this site is shared with Google. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. 100 Units. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. The Computer Science Major Adviser is responsible for approval of specific courses and sequences, and responds as needed to changing course offerings in our program and other programs. We expect this option to be attractive to a fair number of students from every major at UChicago, including the humanities, social sciences and biological sciences.. 100 Units. 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. Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000), and (CMSC 15100 or CMSC 16100 or CMSC 22100 or CMSC 22300 or CMSC 22500 or CMSC 22600) , or by consent. Creating technologies that are inclusive of people in marginalized communities involves more than having technically sophisticated algorithms, systems, and infrastructure. We teach the "Unix way" of breaking a complex computational problem into smaller pieces, most or all of which can be solved using pre-existing, well-debugged, and documented components, and then composed in a variety of ways. Instructor(s): S. KurtzTerms Offered: Spring Students who entered the College prior to Autumn Quarter 2022 and have already completedpart of the recently retired introductory sequence(CMSC12100 Computer Science with Applications I, CMSC15100 Introduction to Computer Science I,CMSC15200 Introduction to Computer Science II, and/or CMSC16100 Honors Introduction to Computer Science I) should plan to follow the academic year 2022 catalog. This course covers the basics of the theory of finite graphs. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Topics include lexical analysis, parsing, type checking, optimization, and code generation. Unsupervised learning and clustering

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