mathematical foundations of machine learning uchicago
It made me realize how powerful data science is in drawing meaningful conclusions and promoting data-driven decision-making, Kielb said. While a student may enroll in CMSC 29700 or CMSC 29900 for multiple quarters, only one instance of each may be counted toward the major. Prerequisite(s): CMSC 11900 or 12200 or CMSC 15200 or CMSC 16200. These include linear and logistic regression and . Equivalent Course(s): CMSC 33210. Instructor(s): K. Mulmuley Basic processes of numerical computation are examined from both an experimental and theoretical point of view. 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. First: some people seem to be misunderstanding 'foundations' in the title. Quizzes (10%): Quizzes will be via canvas and cover material from the past few lectures. Instructor(s): Michael MaireTerms Offered: Winter CMSC 35300 Mathematical Foundations of Machine Learning; MACS 33002 Introduction to Machine Learning . CMSC23220. Terms Offered: Spring 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. 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. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. provides a systematic view of a range of machine learning algorithms, Appropriate for undergraduate students who have taken. During lecture time, we will not do the lectures in the usual format, but instead hold zoom meetings, where you can participate in lab sessions, work with classmates on lab assignments in breakout rooms, and ask questions directly to the instructor. This concise review of linear algebra summarizes some of the background needed for the course. Since it was introduced in 2019, the data science minor has drawn interest from UChicago students across disciplines. 100 Units. 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. Equivalent Course(s): CMSC 30280, MAAD 20380. 100 Units. Recently, The High Commissioner for Human Rights called for states to place moratoriums on AI until it is compliant with human rights. Quizzes: 30%. CMSC23000. 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 . Compilers for Computer Languages. Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. Natural Language Processing. I am delighted that data science will now join the ranks of our majors in the College, introducing students to the rigor and excitement of the higher learning.. Exams (40%): Two exams (20% each). Two exams (20% each). $85.00 Hardcover. Students with prior experience should plan to take the placement exam(s) (described below) to identify the appropriate place to start the sequence. Exams: 40%. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. CMSC27620. Courses fulfilling general education requirements must be taken for quality grades. Time permitting, material on recurrences, asymptotic equality, rates of growth and Markov chains may be included as well. 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. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. Note(s): Students interested in this class should complete this form to request permission to enroll: https://uchicago.co1.qualtrics.com/jfe/form/SV_5jPT8gRDXDKQ26a Ph: 773-702-7891 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. Instructor(s): Laszlo BabaiTerms Offered: Spring Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). Prerequisite(s): CMSC 15400 and (CMSC 27100 or CMSC 27130 or CMSC 37110). 100 Units. 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. Introduction to Complexity Theory. This course covers principles of modern compiler design and implementation. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Instructor(s): G. KindlmannTerms Offered: Winter Foundations of Machine Learning. Prerequisite(s): First year students are not allowed to register for CMSC 12100. You will also put your skills into practice in a semester long group project involving the creation of an interactive system for one of the user populations we study. This course is an introduction to the design and analysis of cryptography, including how "security" is defined, how practical cryptographic algorithms work, and how to exploit flaws in cryptography. Equivalent Course(s): MATH 28410. Courses that fall into this category will be marked as such. Instructor(s): Sarah SeboTerms Offered: Winter Tue., January 17, 2023 | 10:30 AM. Linear algebra strongly recommended; a 200-level Statistics course recommended. Least squares, linear independence and orthogonality 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.. 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. At the end of the sequence, she analyzed the rollout of COVID-19 vaccinations across different socioeconomic groups, and whether the Chicago neighborhoods suffering most from the virus received equitable access. Nonshell scripting languages, in particular perl and python, are introduced, as well as interpreter (#!) This course introduces complexity theory. This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics, etc. We concentrate on a few widely used methods in each area covered. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Introduction to Applied Linear Algebra Vectors, Matrices, and Least Squares by Stephen Boyd and Lieven Vandenberghe(Links to an external site.) It presents standard cryptographic functions and protocols and gives an overview of threats and defenses for software, host systems, networks, and the Web. The textbooks will be supplemented with additional notes and readings. Data Science for Computer Scientists. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. This course provides an introduction to the concepts of parallel programming, with an emphasis on programming multicore processors. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. Covering a story? D: 50% or higher The textbooks will be supplemented with additional notes and readings. Part 1 covered by Mathematics for. CMSC25900. . Note(s): Students who have taken CMSC 15100 may take 16200 with consent of instructor. CMSC25460. Solutions draw from machine learning (especially deep learning), algorithms, linguistics, and social sciences. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. The textbooks will be supplemented with additional notes and readings. Gaussian mixture models and Expectation Maximization This course emphasizes the C Programming Language, but not in isolation. We will have several 3D printers available for use during the class and students will design and fabricate several parts during the course. 100 Units. Equivalent Course(s): ASTR 21400, ASTR 31400, PSMS 31400, CHEM 21400, PHYS 21400. The rst half of the book develops Boolean type theory | a type-theoretic formal foundation for mathematics designed speci cally for this course. The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. In this course, students will learn the fundamental principles, techniques, and tradeoffs in designing the hardware/software interface and hardware components to create a computing system that meets functional, performance, energy, cost, and other specific goals. How can we determine the order of events in a system where we can't assume a single global clock? Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI. ); internet and routing protocols (IP, IPv6, ARP, etc. 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). Honors Graph Theory. CMSC22300. 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. Terms Offered: Autumn Programming Languages. It will cover the basics of training neural networks, including backpropagation, stochastic gradient descent, regularization, and data augmentation. CMSC12100. 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. CMSC12200. 100 Units. Chicago, IL 60637 This is what makes the University of Chicago program uniquely fit to prepare students for their future.. Opportunities for PhDs to work on world-class computer science research with faculty members. Equivalent Course(s): MPCS 51250. The course information in this catalog, with respect to who is teaching which course and in which quarter(s), is subject to change during the academic year. Students may not use AP credit for computer science to meet minor requirements. Computer Science with Applications II. CMSC23010. 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. Terms Offered: Winter CMSC22000. A written report is typically required. Computer science majors must take courses in the major for quality grades. (Note: Prior experience with ML programming not required.) Broadly speaking, Machine Learning refers to the automated identification of patterns in data. 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. 100 Units. 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. Algorithmic questions include sorting and searching, discrete optimization, algorithmic graph theory, algorithmic number theory, and cryptography. Our study of networks will employ formalisms such as graph theory, game theory, information networks, and network dynamics, with the goal of building formal models and translating their observed properties into qualitative explanations. It also touches on some of the legal, policy, and ethical issues surrounding computer security in areas such as privacy, surveillance, and the disclosure of security vulnerabilities. CMSC23218. B+: 87% or higher Instead, we aim to provide the necessary mathematical skills to read those other books. Application: text classification, AdaBoost Networks and Distributed Systems. Students will become familiar with the types and scale of data used to train and validate models and with the approaches to build, tune and deploy machine learned models. 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. Relationships between space and time, determinism and non-determinism, NP-completeness, and the P versus NP question are investigated. This class covers the core concepts of HCI: affordances, mental models, selection techniques (pointing, touch, menus, text entry, widgets, etc), conducting user studies (psychophysics, basic statistics, etc), rapid prototyping (3D printing, etc), and the fundamentals of 3D interfaces (optics for VR, AR, etc). We designed the major specifically to enable students who want to combine data science with another B.A., Biron said. CMSC27800. 100 Units. Introduction to Computer Science I. This course is a basic introduction to computability theory and formal languages. The iterative nature of the design process will require an appreciable amount of time outside of class for completing projects. Matlab, Python, Julia, R). 100 Units. Features and models broadly, the computer science major (or minor). Note(s): Open both to students who are majoring in Computer Science and to nonmajors. CMSC11800. Prerequisite(s): CMSC 15200 or CMSC 16200. 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. Prerequisite(s): CMSC 25300, CMSC 25400, or CMSC 25025. 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. This course aims to introduce computer scientists to the field of bioinformatics. Any 20000-level computer science course taken as an elective beyond requirements for the major may, with consent of the instructor, be taken for P/F grading. They will also wrestle with fundamental questions about who bears responsibility for a system's shortcomings, how to balance different stakeholders' goals, and what societal values computer systems should embed. Equivalent Course(s): STAT 27725. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. Students are required to submit the College Reading and Research Course Form. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) - Financial Math at UChicago literally . The course will be taught at an introductory level; no previous experience is expected. CMSC22001. But the Introduction to Data Science sequence changed her view. Students are expected to have taken calculus and have exposure to numerical computing (e.g. 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. Students who are placed into CMSC14300 Systems Programming I will be invited to sit for the Systems Programming Exam, which will be offered later in the summer. It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. 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. The recent advancement in interactive technologies allows computer scientists, designers, and researchers to prototype and experiment with future user interfaces that can dynamically move and shape-change. Vectors and matrices in machine learning models Prerequisite(s): CMSC 15400. 100 Units. Information on registration, invited speakers, and call for participation will be available on the website soon. Letter grades will be assigned using the following hard cutoffs: A: 93% or higher ); end-to-end protocols (UDP, TCP); and other commonly used network protocols and techniques. Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000) and CMSC 25300. This course is a direct continuation of CMSC 14300. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. By Search . United States 100 Units. 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 final grade will be allocated to the different components as follows: Homework: 30%. Computability topics are discussed (e.g., the s-m-n theorem and the recursion theorem, resource-bounded computation). Enumeration techniques are applied to the calculation of probabilities, and, conversely, probabilistic arguments are used in the analysis of combinatorial structures. 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. Appropriate for graduate students or advanced undergraduates. 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). Advanced Database Systems. Homework exercises will give students hands-on experience with the methods on different types of data. 100 Units. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. It will cover streaming, data cleaning, relational data modeling and SQL, and Machine Learning model training. Courses in the minor must be taken for quality grades, with a grade of C- or higher in each course. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. Topics include DBMS architecture, entity-relationship and relational models, relational algebra, concurrency control, recovery, indexing, physical data organization, and modern database systems. This class offers hands-on experience in learning and employing actuated and shape-changing user interface technologies to build interactive user experiences. Students should consult course-info.cs.uchicago.edufor up-to-date information. Equivalent Course(s): LING 28610. Prerequisite(s): CMSC 15400 100 Units. Scalar first-order hyperbolic equations will be considered. The PDF will include all information unique to this page. But for data science, experiential learning is fundamental. In order for you to be successful in engineering a functional PCB, we will (1) review digital circuits and three microcontrollers (ATMEGA, NRF, SAMD); (2) use KICAD to build circuit schematics; (3) learn how to wire analog/digital sensors or actuators to our microcontroller, including SPI and I2C protocols; (4) use KICAD to build PCB schematics; (5) actually manufacture our designs; (6) receive in our hands our PCBs from factory; (7) finally, learn how to debug our custom-made PCBs. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. Final: Wednesday, March 13, 6-8pm in KPTC 120. The University of Chicago's eight-week Artificial Intelligence and Machine Learning course guides participants through the mathematical and theoretical background necessary to . Reading and Research in Computer Science. 100 Units. Machine Learning. From linear algebra and multivariate This course will provide an introduction to neural networks and fundamental concepts in deep learning. Introduction to Cryptography. Honors Combinatorics. (And how do we ensure this in the presence of failures?) Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. These scientific "miracles" are robust, and provide a valuable longer-term understanding of computer capabilities, performance, and limits to the wealth of computer scientists practicing data science, software development, or machine learning. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. 100 Units. Decision trees Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) Students will continue to use Python, and will also learn C and distributed computing tools and platforms, including Amazon AWS and Hadoop. Standard machine learning (ML) approaches often assume that the training and test data follow similar distributions, without taking into account the possibility of adversaries manipulating either distribution or natural distribution shifts. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Introduction to Numerical Partial Differential Equations. CMSC27530. These courses may be courses taken for the major or as electives. Students are encouraged, but not required, to fulfill this requirement with a physics sequence. discriminatory, and is the algorithm the right place to look? Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. The Lasso and proximal point algorithms B: 83% or higher 100 Units. In this course, students will develop a deeper understanding of what a computer does when executing a program. CMSC15200. CMSC23240. Students will gain experience applying neural networks to modern problems in computer vision, natural language processing, and reinforcement learning. Through multiple project-based assignments, students practice the acquired techniques to build interactive tangible experiences of their own. Instructor(s): Stuart KurtzTerms Offered: TBD CMSC25400. In recent offerings, students have written a course search engine and a system to do speaker identification. What makes an algorithm Prerequisite(s): CMSC 16100, or CMSC 15100 and by consent. Kernel methods and support vector machines Equivalent Course(s): MAAD 13450, HMRT 23450. 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. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. The course examines in detail topics in both supervised and unsupervised learning. Hardcopy ( MIT Press, Amazon ). 100 Units. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. This course introduces the basic concepts and techniques used in three-dimensional computer graphics. A major goal of this course is to enable students to formalize and evaluate theoretical claims. 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. The course will include bi-weekly programming assignments, a midterm examination, and a final. Computer Science with Applications III. Model selection, cross-validation Topics include propositional and predicate logic and the syntactic notion of proof versus the semantic notion of truth (e.g., soundness, completeness). Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. Programming in a functional language (currently Haskell), including higher-order functions, type definition, algebraic data types, modules, parsing, I/O, and monads. The course will be fast moving and will involve weekly program assignments. Introduction to Computer Systems. Machine learning topics include the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Experience with mathematical proofs. The course will unpack and re-entangle computational connections and data-driven interactions between people, built space, sensors, structures, devices, and data. Some methods for solving linear algebraic systems will be used. This course deals with finite element and finite difference methods for second-order elliptic equations (diffusion) and the associated parabolic and hyperbolic equations. To earn a BS in computer science, the general education requirement in the physical sciences must be satisfied by completing a two-quarter sequence chosen from the General Education Sequences for Science Majors. This story was first published by the Department of Computer Science. Note(s): anti-requisites: CMSC 25900, DATA 25900. 100 Units. 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 . Join us in-person and online for seminars, panels, hack nights, and other gatherings on the frontier of computer science. Mathematical Foundations of Machine Learning - linear algebra (0) 2022.12.24: How does AI calculate the percentage in binary language system? Winter CMSC25025. 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. Teaching staff: Lang Yu
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