CMSC14300. 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. Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. Practical exercises in writing language transformers reinforce the the theory. Prerequisite(s): MATH 25400 or MATH 25700 or (CMSC 15400 and (MATH 15910 or MATH 15900 or MATH 19900 or MATH 16300)) Reviewer 1 Report. Prerequisite(s): CMSC 11900 or 12200 or CMSC 15200 or CMSC 16200. Building upon the data science minor and the Introduction to Data Science sequence taught by Franklin and Dan Nicolae, professor and chair in the Department of Statistics and the College, the major will include new courses and emphasize research and application. All rights reserved. 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. Students should consult the major adviser with questions about specific courses they are considering taking to meet the requirements. CMSC28000. Foundations of Machine Learning. This hands-on, authentic learning experience offers the real possibility for the field to grow in a manner that actually reflects the population it purports to engage, with diverse scientists asking novel questions from a wide range of viewpoints.. STAT 37500: Pattern Recognition (Amit) Spring. 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. Computing Courses - 250 units. Computer Science offers an introductory sequence for students interested in further study in computer science: Students with no prior experience in computer science should plan to start the sequence at the beginning in CMSC14100 Introduction to Computer Science I. Matlab, Python, Julia, or R). Prerequisite(s): A year of calculus (MATH 15300 or higher), a quarter of linear algebra (MATH 19620 or higher), and CMSC 10600 or higher; or consent of instructor. 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. 100 Units. UChicago Harris Campus Visit. Instructor(s): Michael MaireTerms Offered: Winter The course will demonstrate how computer systems can violate individuals' privacy and agency, impact sub-populations in disparate ways, and harm both society and the environment. 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. Computer science majors must take courses in the major for quality grades. 100 Units. The class will also introduce students to basic aspects of the software development lifecycle, with an emphasis on software design. Prerequisite(s): CMSC 15200 or CMSC 16200. This course is a basic introduction to computability theory and formal languages. Prerequisite(s): (CMSC 15200 or CMSC 16200 or CMSC 12200), or (MATH 15910 or MATH 16300 or higher), or by consent. 100 Units. 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. 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. It will explore network design principles, spanning multilayer perceptrons, convolutional and recurrent architectures, attention, memory, and generative adversarial networks. 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. Least squares, linear independence and orthogonality Equivalent Course(s): CMSC 33230. CMSC12100-12200-12300. 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. 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. This concise review of linear algebra summarizes some of the background needed for the course. 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. Students who place out of CMSC14400 Systems Programming II based on the Systems Programming Exam are required to take an additional computer science elective course for a total of six electives, as well as the additional Programming Languages and Systems Sequence course mentioned above. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Linear classifiers Terms Offered: Winter All paths prepare students with the toolset they need to apply these skills in academia, industry, nonprofit organizations, and government. Terms Offered: Winter 100 Units. Note: students can use at most one of CMSC 25500 and TTIC 31230 towards the computer science major. provides a systematic view of a range of machine learning algorithms, Appropriate for undergraduate students who have taken. Prerequisite(s): CMSC 20300 Machine Learning and Large-Scale Data Analysis. Sensing, actuation, and mediation capabilities of mobile devices are transforming all aspects of computing: uses, networking, interface, form, etc. This course meets the general education requirement in the mathematical sciences. The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring. Equivalent Course(s): CMSC 33218, MAAD 23218. 100 Units. discriminatory, and is the algorithm the right place to look? Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Prerequisite(s): CMSC 15400 The honors version of Theory of Algorithms covers topics at a deeper level. Instructor(s): A. RazborovTerms Offered: Autumn CMSC25422. Equivalent Course(s): MPCS 54233. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. 100 Units. 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. Computer Architecture. Machine Learning in Medicine. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. CMSC 35300 Mathematical Foundations of Machine Learning; MACS 33002 Introduction to Machine Learning . No previous biology coursework is required or expected. 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. CMSC28540. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. Opportunities for PhDs to work on world-class computer science research with faculty members. Pass/Fail Grading:A grade of P is given only for work of C- quality or higher. The Leibniz Institute SAFE is seeking to fill the position of a Research Assistant (m/f/d), 50% Position, salary group E13 TV-H. We are looking for a research assistant for the project "From Machine Learning to Machine Teaching (ML2MT) - Making Machines AND Humans Smarter" funded by Volkswagen Foundation with Prof. Pelizzon being one of . Bookmarks will appear here. 100 Units. Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. Microsoft. Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. This course introduces complexity theory. This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. 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] Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. The Data Science Clinic will provide an understanding of the life cycle of a real-world data science project, from inception and gathering, to modeling and iteration to engineering and implementation, said David Uminsky, executive director of the UChicago Data Science Initiative. Least squares, linear independence and orthogonality Dependent types. Ethics, Fairness, Responsibility, and Privacy in Data Science. Final: Wednesday, March 13, 6-8pm in KPTC 120. Logistic regression This is a practical programming course focused on the basic theory and efficient implementation of a broad sampling of common numerical methods. CMSC14200. Prerequisite(s): CMSC 15400. Programming Languages. 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. UChicago Financial Mathematics. 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. This course will focus on analyzing complex data sets in the context of biological problems. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. 100 Units. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Computer Architecture for Scientists. Instructor(s): B. SotomayorTerms Offered: Winter Linear algebra strongly recommended; a 200-level Statistics course recommended. Prerequisite(s): CMSC 20300 or CMSC 20600 or CMSC 21800 or CMSC 22000 or CMSC 22001 or CMSC 23000 or CMSC 23200 or CMSC 23300 or CMSC 23320 or CMSC 23400 or CMSC 23500 or CMSC 23900 or CMSC 25025. A major goal of this course is to enable students to formalize and evaluate theoretical claims. Instructor(s): Austin Clyde, Pozen Center for Human Rights Graduate LecturerTerms Offered: Autumn | Learn more about Rohan Kumar's work experience, education . 100 Units. This is not a book about foundations in the sense that this is where you should start if you want to learn about machine learning. Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Figure 4.1: An algorithmic framework for online strongly convex programming. Actuated User Interfaces and Technology. Theory of Algorithms. Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 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. Programming Proofs. Networks also help us understand properties of financial markets, food webs, and web technologies. United States Linear classifiers Matlab, Python, Julia, R). This introduction to quantum computing will cover the key principles of quantum information science and how they relate to quantum computing as well as the notation and operations used in QIS. Exams (40%): Two exams (20% each). Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. This course explores new technologies driving mobile computing and their implications for systems and society. 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