Prerequisite(s): CMSC 25300, CMSC 25400, or CMSC 25025. Furthermore, the course will examine how memory is organized and structured in a modern machine. Students are expected to have taken calculus and have exposure to numerical computing (e.g. 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. Introduction to Formal Languages. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) Broadly speaking, Machine Learning refers to the automated identification of patterns in data. The new major is part of the University of Chicago Data Science Initiative, a coordinated, campus-wide plan to expand education, research, and outreach in this fast-growing field. Honors Theory of Algorithms. broadly, the computer science major (or minor). Existing methods for analyzing genomes, sequences and protein structures will be explored, as well related computing infrastructure. Features and models Note(s): This course meets the general education requirement in the mathematical sciences. 7750: Mathematical Foundations of Machine Learning (Fall 2022) Description: This course for beginning graduate students develops the mathematical foundations of machine learning, rigorously introducing students to modeling and representation, statistical inference, and optimization. Prerequisite(s): By consent of instructor and approval of department counselor. We strongly encourage all computer science majors to complete their theory courses by the end of their third year. Prerequisite(s): CMSC 15400 )" Skip to search form Skip to main content Skip to account menu. Students who earn the BS degree build strength in an additional field by following an approved course of study in a related area. Students may petition to have graduate courses count towards their specialization via this same page. From linear algebra and multivariate The course will be organized primarily around the development of a class-wide software project, with students organized into teams. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. Instructor(s): LopesTerms Offered: Spring As such it has been a fertile ground for new statistical and algorithmic developments. It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. 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. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. This course is an introduction to database design and implementation. Relationships between space and time, determinism and non-determinism, NP-completeness, and the P versus NP question are investigated. CMSC23218. Terms Offered: Autumn Students should consult the major adviser with questions about specific courses they are considering taking to meet the requirements. Note(s): Students who have taken CMSC 15100 may take 16200 with consent of instructor. Class place and time: Mondays and Wednesdays, 3-4:15pm, Office hours: Mondays, 1:30-2:30pm when classes are in session, Piazza: https://piazza.com/uchicago/winter2019/cmsc25300/home, TAs: Zewei Chu, Alexander Hoover, Nathan Mull, Christopher Jones. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. 100 Units. The course will unpack and re-entangle computational connections and data-driven interactions between people, built space, sensors, structures, devices, and data. 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 who are interested in the visual arts or design should consider CMSC11111 Creative Coding. CMSC23700. Prerequisite(s): By consent of instructor and approval of department counselor. The course culminates in the production and presentation of a capstone interactive artwork by teams of computer scientists and artists; successful products may be considered for prototyping at the MSI. Students with prior experience should plan to take the placement exam(s) (described below) to identify the appropriate place to start the sequence. 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. But the Introduction to Data Science sequence changed her view. UChicago Financial Mathematics. The course covers both the foundations of 3D graphics (coordinate systems and transformations, lighting, texture mapping, and basic geometric algorithms and data structures), and the practice of real-time rendering using programmable shaders. Students may petition to take more advanced courses to fulfill this requirement. - Financial Math at UChicago literally . Further topics include proof by induction; number theory, congruences, and Fermat's little theorem; relations; factorials, binomial coefficients and advanced counting; combinatorial probability; random variables, expected value, and variance; graph theory and trees. Numerical Methods. Prerequisite(s): (CMSC 12300 or CMSC 15400), or MAtH 16300 or higher, or by consent. Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. Networks help explain phenomena in such technological, social, and biological domains as the spread of opinions, knowledge, and infectious diseases. Instructor(s): ChongTerms Offered: Spring 100 Units. towards the Machine Learning specialization, and, more Topics include: algebraic datatypes, an elegant language for describing and manipulating domain-specific data; higher-order functions and type polymorphism, expressive mechanisms for abstracting programs; and a core set of type classes, with strong connections to category theory, that serve as a foundational and practical basis for mixing pure functions with stateful and interactive computations. Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Matlab, Python, Julia, R). F: less than 50%. There are several high-level libraries like TensorFlow, PyTorch, or scikit-learn to build upon. Instructor consent required. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. CMSC28000. 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. The objective of this course is to train students to be insightful users of modern machine learning methods. The statistical foundations of machine learning. This course emphasizes mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Note(s): Students who have taken CMSC 11800, STAT 11800, CMSC 12100, CMSC 15100, or CMSC 16100 are not allowed to register for CMSC 11111. Helping someone suffering from schizophrenia determine reality; an alarm to help maintain distance during COVID; adding a fun gamification element to exercise. Exams: 40%. The course also emphasizes the importance of collaboration in real-world software development, including interpersonal collaboration and team management. Topics include lexical analysis, parsing, type checking, optimization, and code generation. Prerequisite(s): CMSC 15400 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. Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries. In addition, we will discuss advanced topics regarding recent research and trends. Honors Introduction to Computer Science II. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. Computer Architecture. 100 Units. The course discusses both the empirical aspects of software engineering and the underlying theory. Equivalent Course(s): MATH 28530. Link: https://canvas.uchicago.edu/courses/35640/, Discussion and Q&A: Via Ed Discussion (link provided on Canvas). 100 Units. Introduction to Data Engineering. Inventing, Engineering and Understanding Interactive Devices. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. provided on Canvas). The goal of this course is to provide a foundation for further study in computer security and to help better understand how to design, build, and use computer systems more securely. Reviewer 1 Report. Prerequisite(s): CMSC 12100 Students will also gain basic facility with the Linux command-line and version control. Honors Introduction to Complexity Theory. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Defining and building the future of computer science, from theory to applications and from science to society. Topics include automata theory, regular languages, context-free languages, and Turing machines. Equivalent Course(s): CMSC 30600. Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Introduction to Complexity Theory. 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. By This course covers computational methods for structuring and analyzing data to facilitate decision-making. Creating technologies that are inclusive of people in marginalized communities involves more than having technically sophisticated algorithms, systems, and infrastructure. The honors version of Discrete Mathematics covers topics at a deeper level. This course meets the general education requirement in the mathematical sciences. This course introduces complexity theory. Contacts | Program of Study | Where to Start | Placement | Program Requirements | Summary of Requirements | Specializations | Grading | Honors | Minor Program in Computer Science | Joint BA/MS or BS/MS Program | Graduate Courses | Schedule Changes | Courses, Department Website: https://www.cs.uchicago.edu. CMSC28100. This course leverages human-computer interaction and the tools, techniques, and principles that guide research on people to introduce you to the concepts of inclusive technology design. In this hands-on, practical course, you will design and build functional devices as a means to learn the systematic processes of engineering and fundamentals of design and construction. Equivalent Course(s): MATH 27800. 100 Units. There are roughly weekly homework assignments (about 8 total). Kernel methods and support vector machines Prerequisite(s): CMSC 14300 or CMSC 15200. Introduction to Bioinformatics. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. Students are expected to have taken calculus and have exposureto numerical computing (e.g. 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. Creative Coding. Matrix Methods in Data Mining and Pattern Recognition by Lars Elden. 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. Exams (40%): Two exams (20% each). Lecture 1: Intro -- Mathematical Foundations of Machine Learning CMSC 25025 Machine Learning and Large-Scale Data Analysis CMSC 25040 Introduction to Computer Vision CMSC 25300 Mathematical Foundations of Machine Learning CMSC 25400 Machine Learning CMSC 25440 Machine Learning in Medicine CMSC 25460 Introduction to Optimization CMSC 25500 Introduction to Neural Networks CMSC 25700 Natural Language Processing 100 Units. Topics covered will include applications of machine learning models to security, performance analysis, and prediction problems in systems; data preparation, feature selection, and feature extraction; design, development, and evaluation of machine learning models and pipelines; fairness, interpretability, and explainability of machine learning models; and testing and debugging of machine learning models. 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.. It is typically taken by students who have already taken TTIC31020or a similar course, but is sometimes appropriate as a first machine learning course for very mathematical students that prefer understanding a topic through definitions and theorems rather then examples and applications. The National Science Foundation (NSF) Directorates for Computer and Information Science and Engineering (CISE), Engineering (ENG), Mathematical and Physical Sciences (MPS), and Social, Behavioral and Economic Sciences (SBE) promote interdisciplinary research in Mathematical and Scientific Foundations of Deep Learning and related areas (MoDL+). CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. CMSC16100-16200. Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. Certificate Program. Where do breakthrough discoveries and ideas come from? Equivalent Course(s): STAT 11900, DATA 11900. Basic apprehension of calculus and linear algebra is essential. 100 Units. CMSC25440. The class will rigorously build up the two pillars of modern . This course will not be offered again. Data science provides tools for gaining insight into specific problems using data, through computation, statistics and visualization. Note A broad background on probability and statistical methodology will be provided. Students will explore more advanced concepts in computer science and Python programming, with an emphasis on skills required to build complex software, such as object-oriented programming, advanced data structures, functions as first-class objects, testing, and debugging. Equivalent Course(s): MATH 27700. This course introduces the fundamental concepts and techniques in data mining, machine learning, and statistical modeling, and the practical know-how to apply them to real-world data through Python-based software. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Now supporting the University of Chicago. Unsupervised learning and clustering 100 Units. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. ing machine learning. Equivalent Course(s): STAT 27725. Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. Students should consult course-info.cs.uchicago.edufor up-to-date information. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Note(s): This course meets the general education requirement in the mathematical sciences. Terms Offered: Winter To earn a BA in computer science any sequence or pair of courses approved by the Physical Sciences Collegiate Division may be used to complete the general education requirement in the physical sciences. with William Howell. 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. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. 100 Units. We will have several 3D printers available for use during the class and students will design and fabricate several parts during the course. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation and communication of results. It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. CMSC27700. Note(s): This course is offered in alternate years. 100 Units. 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. B-: 80% or higher Prerequisite(s): (CMSC 15200 or CMSC 16200 or CMSC 12200), or (MATH 15910 or MATH 16300 or higher), or by consent. Neural networks and backpropagation, Density estimation and maximum likelihood estimation Prerequisite(s): MATH 25400 or MATH 25700 or (CMSC 15400 and (MATH 15910 or MATH 15900 or MATH 19900 or MATH 16300)) ); end-to-end protocols (UDP, TCP); and other commonly used network protocols and techniques. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. The course will include bi-weekly programming assignments, a midterm examination, and a final. Terms Offered: Winter The rst half of the book develops Boolean type theory | a type-theoretic formal foundation for mathematics designed speci cally for this course. Fostering an inclusive environment where students from all backgrounds can achieve their highest potential. Students who earn the BA are prepared either for graduate study in computer science or a career in industry. Equivalent Course(s): MATH 28410. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. This course introduces the principles and practice of computer security. The course project will revolve around the implementation of a mini x86 operating system kernel. Formal constructive mathematics. CMSC23300. Prerequisite(s): Completion of the general education requirement in the mathematical sciences, and familiarity with basic concepts of probability at the high school level. 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. This course covers the basics of computer systems from a programmer's perspective. Compilers for Computer Languages. 100 Units. 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. Foundations of Machine Learning. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. that at most one of CMSC 25500 and TTIC 31230 count Prerequisite(s): CMSC 20300 . The course will also cover special topics such as journaling/transactions, SSD, RAID, virtual machines, and data-center operating systems. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Scientific Visualization. Engineering Interactive Electronics onto Printed Circuit Boards. 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. 5747 South Ellis Avenue Instructor(s): A. RazborovTerms Offered: Autumn CMSC25025. Appropriate for graduate students or advanced undergraduates. Prerequisite(s): CMSC 15400. STAT 37750: Compressed Sensing (Foygel-Barber) Spring. CMSC22000. CMSC23240. (Links to an external site.) Students who major in computer science have the option to complete one specialization. Visit our page for journalists or call (773) 702-8360. Computer Science with Applications I. 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. 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. 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. This course covers the basics of the theory of finite graphs. 100 Units. Topics include DBMS architecture, entity-relationship and relational models, relational algebra, concurrency control, recovery, indexing, physical data organization, and modern database systems. Quizzes (10%): Quizzes will be via canvas and cover material from the past few lectures. Advanced Algorithms. 100 Units. Appropriate for graduate students oradvanced undergraduates. I had always viewed data science as something very much oriented toward people passionate about STEM, but the data science sequence really framed it as a tool that anyone in any discipline could employ, to tell stories using data and uncover insights in a more quantitative and rigorous way.. Request form available online https://masters.cs.uchicago.edu Equivalent Course(s): MPCS 51250. CMSC21010. Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. and two other courses from this list, Bachelors thesis in computer security, approved as such, Computer Systems: three courses from this list, over and above those taken to fulfill the programming languages and systems requirement, CMSC22240 Computer Architecture for Scientists, CMSC23300 Networks and Distributed Systems, CMSC23320 Foundations of Computer Networks, CMSC23500 Introduction to Database Systems, Bachelors thesis in computer systems, approved as such, Data Science: CMSC21800 Data Science for Computer Scientists and two other courses from this list, CMSC25025 Machine Learning and Large-Scale Data Analysis, CMSC25300 Mathematical Foundations of Machine Learning, Bachelors thesis in data science, approved as such, Human Computer Interaction:CMSC20300 Introduction to Human-Computer Interaction 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. CMSC25700. We designed the major specifically to enable students who want to combine data science with another B.A., Biron said. Prerequisite(s): CMSC 25300 or CMSC 35300 or STAT 24300 or STAT 24500 Professor, Departments of Computer Science and Statistics, Assistant Professor, Department of Computer Science, Edward Carson Waller Distinguished Service Professor Emeritus, Departments of Computer Science and Linguistics, Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science, Assistant Professor, Department of Computer Science, College, Assistant Professor, Computer Science (starting Fall 2023), Associate Professor, Department of Computer Science, Associate Professor, Departments of Computer Science and Statistics, Associate Professor, Toyota Technological Institute, Professor, Toyota Technological Institute, Assistant Professor, Computer Science and Data Science, Assistant Professor, Toyota Technological Institute. 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. B: 83% or higher Equivalent Course(s): MAAD 21111. The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. 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. In this course, we will explore the use of proof assistants, computer programs that allow us to write, automate, and mechanically check proofs. 100 Units. Midterm: Wednesday, Feb. 6, 6-8pm in KPTC 120 Equivalent Course(s): CMSC 30370, MAAD 20370. 3D Printing), electronics (Arduino microcontroller), and actuator control (utilizing different kinds of motors). For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. By using this site, you agree to its use of cookies. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. Semantic Scholar's Logo. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Equivalent Course(s): MAAD 25300. CMSC27410. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries. Equivalent Course(s): MATH 28100. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. 1. Please be aware that course information is subject to change, and the catalog does not necessarily reflect the most recent information. CMSC23200. Cambridge University Press, 2020. You will learn about different underserved and marginalized communities such as children, the elderly, those needing assistive technology, and users in developing countries, and their particular needs. Prerequisite(s): (CMSC 12200 or CMSC 15200 or CMSC 16200) and (CMSC 27200 or CMSC 27230 or CMSC 37000). CMSC25900. 100 Units. Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. CMSC12100-12200-12300. Some methods for solving linear algebraic systems will be used. Develops data-driven systems that derive insights from network traffic and explores how network traffic can reveal insights into human behavior. 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. Microsoft. The book is available at published by Cambridge University Press (published April 2020). Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. It requires a high degree of mathematical maturity, typical of mathematically-oriented CS and statistics PhD students or math graduates.

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