Computer science majors must take courses in the major for quality grades. CMSC23300. Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. Its really inspiring that I can take part in a field thats rapidly evolving.. Equivalent Course(s): MATH 28410. Basic topics include processes, threads, concurrency, synchronization, memory management, virtual memory, segmentation, paging, caching, process and I/O scheduling, file systems, storage devices. Team projects are assessed based on correctness, elegance, and quality of documentation. 100 Units. Placement into MATH 15100 or completion of MATH 13100. 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. CMSC12100. This course will examine how to design for security and privacy from a user-centered perspective by combining insights from computer systems, human-computer interaction (HCI), and public policy. Spring STAT 34000: Gaussian Processes (Stein) Spring. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. Programming in a functional language (currently Haskell), including higher-order functions, type definition, algebraic data types, modules, parsing, I/O, and monads. 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. files that use the command-line version of DrScheme. 100 Units. A Pass grade is given only for work of C- quality or higher. It requires a high degree of mathematical maturity, typical of mathematically-oriented CS and statistics PhD students or math graduates. A written report is . Through both computer science and studio art, students will design algorithms, implement systems, and create interactive artworks that communicate, provoke, and reframe pervasive issues in modern privacy and security. 5801 S. Ellis Ave., Suite 120, Chicago, IL 60637, The Day Tomorrow Began series explores breakthroughs at the University of Chicago, Institute of Politics to celebrate 10-year anniversary with event featuring Secretary Antony Blinken, UChicago librarian looks to future with eye on digital and traditional resources, Six members of UChicago community to receive 2023 Diversity Leadership Awards, Scientists create living smartwatch powered by slime mold, Chicago Booths 2023 Economic Outlook to focus on the global economy, Prof. Ian Foster on laying the groundwork for cloud computing, Maroons make history: UChicago mens soccer team wins first NCAA championship, Class immerses students in monochromatic art exhibition, Piece of earliest known Black-produced film found hiding in plain sight, I think its important for young girls to see women in leadership roles., Reflecting on a historic 2022 at UChicago. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. CMSC 29700. Prerequisite(s): CMSC 27100, CMSC 27130, or CMSC 37110, or MATH 20400 or MATH 20800. Terms Offered: Spring This course is cross-listed between CS, ECE, and . STAT 30900 / CMSC 3781: Mathematical Computation I Matrix Computation, STAT 31015 / CMSC 37811: Mathematical Computation II Convex Optimization, STAT 37710 / CMSC 35400: Machine Learning, TTIC 31150/CMSC 31150: Mathematical Toolkit. Massive Open Online Courses (MOOCs) were created to bring education to those without access to universities, yet most of the students who succeed in them are those who are already successful in the current educational model. We reserve the right to curve the grades, but only in a fashion that would improve the grade earned by the stated rubric. CMSC27502. Parallel Computing. Topics include number theory, Peano arithmetic, Turing compatibility, unsolvable problems, Gdel's incompleteness theorem, undecidable theories (e.g., the theory of groups), quantifier elimination, and decidable theories (e.g., the theory of algebraically closed fields). CMSC23320. STAT 37601/CMSC 25025: Machine Learning and Large Scale Data Analysis (Lafferty) Spring. Instructor consent required. Successfully created an ML model with Python and Azure, which can predict whether or not a . 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. This course covers the basics of the theory of finite graphs. 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. Equivalent Course(s): MATH 28130. Note(s): This course meets the general education requirement in the mathematical sciences. This sequence, which is recommended for all students planning to take more advanced courses in computer science, introduces computer science mostly through the study of programming in functional (Scheme) and imperative (C) programming languages. The Elements of Statistical Learning (second edition); by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. Introduction to Data Science II. Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. No matter where I go after graduation, I can help make sense of chaos in whatever kind of environment I'm working in.. The vast amounts of data produced in genomics related research has significantly transformed the role of biological research. Prerequisite(s): CMSC 25300, CMSC 25400, CMSC 25025, or TTIC 31020. CMSC27410. Our emphasis is on basic principles, mathematical models, and efficient algorithms established in modern computer vision. 5747 South Ellis Avenue 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. 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. The course examines in detail topics in both supervised and unsupervised learning. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. Equivalent Course(s): MAAD 13450, HMRT 23450. The kinds of things you will learn may include mechanical design and machining, computer-aided design, rapid prototyping, circuitry, electrical measurement methods, and other techniques for resolving real-world design problems. The topics covered in this course will include software, data mining, high-performance computing, mathematical models and other areas of computer science that play an important role in bioinformatics. Terms Offered: Autumn Lecture 1: Intro -- Mathematical Foundations of Machine Learning B-: 80% or higher Prerequisite(s): CMSC 15400. CMSC25500. Prerequisite(s): CMSC 14300, or placement into CMSC 14400, is a prerequisite for taking this course. Mathematical Foundations. Systems Programming I. 100 Units. mathematical foundations of machine learning uchicago. 100 Units. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Learn more about the course offerings in the Foundations Year below: Foundations YearAutumn Quarter A-: 90% or higher This first course of the two would . We will focus on designing and laying out the circuit and PCB for our own custom-made I/O devices, such as wearable or haptic devices. Foundations of Machine Learning. Mobile computing is pervasive and changing nearly every aspect of society. CMSC25700. 2017 The University of Chicago
100 Units. CMSC22001. CMSC27230. Natural Language Processing. Introduction to Software Development. How do we ensure that all the machines have a consistent view of the system's state? Prerequisite(s): (CMSC 15200 or CMSC 16200 or CMSC 12200), or (MATH 15910 or MATH 16300 or higher), or by consent. 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. We will introduce the machine learning methods as we go, but previous familiarity with machine learning will be helpful. The course also emphasizes the importance of collaboration in real-world software development, including interpersonal collaboration and team management. 100 Units. Cambridge University Press, 2020. Learnt data science, learn its content, discipline construction, applications and employment prospects. 100 Units. Prerequisite(s): CMSC 15400. 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. Exams (40%): Two exams (20% each). 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. The major requires five additional elective computer science courses numbered 20000 or above. Hardcopy ( MIT Press, Amazon ). The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. Matlab, Python, Julia, or R). PhD students in other departments, as well as masters students and undergraduates, with sufficient mathematical and programming background, are also welcome to take the course, at the instructors permission. Note(s): A more detailed course description should be available later. Collaboration both within and across teams will be essential to the success of the project. This is a project oriented course in which students will construct a fully working compiler, using Standard ML as the implementation language. Prof. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. This is a practical programming course focused on the basic theory and efficient implementation of a broad sampling of common numerical methods. 100 Units. The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. On the mathematical foundations of learning F. Cucker, S. Smale Published 5 October 2001 Computer Science Bulletin of the American Mathematical Society (1) A main theme of this report is the relationship of approximation to learning and the primary role of sampling (inductive inference). CMSC13600. Topics include program design, control and data abstraction, recursion and induction, higher-order programming, types and polymorphism, time and space analysis, memory management, and data structures including lists, trees, and graphs. Computers for Learning. Topics include DBMS architecture, entity-relationship and relational models, relational algebra, concurrency control, recovery, indexing, physical data organization, and modern database systems. This is a rigorous mathematical course providing an analytic view of machine learning. Plan accordingly. Big Brains podcast: Is the U.S. headed toward another civil war? Networks help explain phenomena in such technological, social, and biological domains as the spread of opinions, knowledge, and infectious diseases. 100 Units. arge software systems are difficult to build. 3. CMSC22300. B+: 87% or higher Functional Programming. 432 pp., 7 x 9 in, 55 color illus., 40 b&w illus. Data Analytics. 100 Units. CMSC23700. Some methods for solving linear algebraic systems will be used. Students should consult course-info.cs.uchicago.edufor up-to-date information. Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. 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 system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. 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. This course focuses on the principles and techniques used in the development of networked and distributed software. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. We will explore these concepts with real-world problems from different domains. For up-to-date information on our course offerings, please consult course-info.cs.uchicago.edu. Instructor(s): A. ElmoreTerms Offered: Winter This course covers computational methods for structuring and analyzing data to facilitate decision-making. Introduction to Quantum Computing. While this course is not a survey of different programming languages, we do examine the design decisions embodied by various popular languages in light of their underlying formal systems. Please be aware that course information is subject to change, and the catalog does not necessarily reflect the most recent information. Note(s): Students can use at most one of CMSC 25500 and TTIC 31230 towards a CS major or CS minor. In this class, we critically examine emergent technologies that might impact the future generations of computing interfaces, these include: physiological I/O (e.g., brain and muscle computer interfaces), tangible computing (giving shape and form to interfaces), wearable computing (I/O devices closer to the user's body), rendering new realities (e.g., virtual and augmented reality), haptics (giving computers the ability to generate touch and forces) and unusual auditory interfaces (e.g., silent speech and microphones as sensors). Machine Learning - Python Programming. Outline: This course is an introduction to key mathematical concepts at the heart of machine learning. 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. How can we determine the order of events in a system where we can't assume a single global clock? 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. Advanced Algorithms. We compliment the lectures with weekly programming assignments and two larger projects, in which we build/program/test user-facing interactive systems. CMSC11000. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Please note that a course that is counted towards a specialization may not also be counted towards a major sequence requirement (i.e., Programming Languages and Systems, or Theory). 100 Units. Semantic Scholar's Logo. 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. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. The present review "Genetic redundancy in rye shows in a variety of ways" by Vershinin et al., investigated the genomic organization of 19 rye chromosomes with a description of the molecular mechanisms contributing the evolution of genomic structure. CMSC16200. Honors Introduction to Computer Science I. Instructor(s): S. LuTerms Offered: Autumn CMSC25610. Scalar first-order hyperbolic equations will be considered. - Bayesian Inference and Machine Learning I and II from Gordon Ritter. B: 83% or higher Generally offered alternate years. Prerequisite(s): CMSC 15400 Helping someone suffering from schizophrenia determine reality; an alarm to help maintain distance during COVID; adding a fun gamification element to exercise. 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. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. STAT 37400: Nonparametric Inference (Lafferty) Fall. Note(s): Open both to students who are majoring in Computer Science and to nonmajors. towards the Machine Learning specialization, and, more Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction 100 Units. To become a successful Data scientist, one should have skills in three major areas: Mathematics; Technology and Hacking; Strong Business Acumen The final grade will be allocated to the different components as follows: Homework (50% UG, 40% G): There are roughly weekly homework assignments (about 8 total). The course is also intended for students outside computer science who are experienced with programming and computing with scientific data. UChicago (9) iversity (9) SAS Institute (9) . Introduction to Computer Science II. (i) A coherent three-quarter sequence in an independent domain of knowledge to which Data Science can be applied. In the context of the C language, the course will revisit fundamental data structures by way of programming exercises, including strings, arrays, lists, trees, and dictionaries. No experience in security is required. Prerequisite(s): CMSC 12200, CMSC 15200 or CMSC 16200. Algorithmic questions include sorting and searching, graph algorithms, elementary algorithmic number theory, combinatorial optimization, randomized algorithms, as well as techniques to deal with intractability, like approximation algorithms. Many of these fundamental problems were identified and solved over the course of several decades, starting in the 1970s. Quizzes will be via canvas and cover material from the past few lectures. Discrete Mathematics. Prerequisite(s): (CMSC 27100 or CMSC 27130 or CMSC 37000) and CMSC 25300. The combination of world-class liberal arts education, sophisticated theoretical examination, and exploration of relevant, real-world problems as integral to the major is invaluable for graduates to establish a rewarding career. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. 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. The textbooks will be supplemented with additional notes and readings. This story was first published by the Department of Computer Science. 100 Units. CMSC25300. The department also offers a minor. CMSC22000. Students who major in computer science have the option to complete one specialization. Topics will include usable authentication, user-centered web security, anonymity software, privacy notices, security warnings, and data-driven privacy tools in domains ranging from social media to the Internet of Things. Please note that a course that is counted towards a specialization may not also be counted towards a major sequence requirement (i.e., Programming Languages and Systems, or Theory). Reading and Research in Computer Science. During Foundations Year, students also take a number of Content and Methods Courses in literacy, math, science, and social science to fulfill requirements for both the elementary and middle grades endorsement pathways. Introduction to Creative Coding. His group developed mathematical models based on this data and then began using machine-learning methods to reveal new information about proteins' basic design rules. Note Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. Mathematical topics covered include linear equations, regression, regularization,the singular value decomposition, and iterative algorithms. )" Skip to search form Skip to main content Skip to account menu. Students who are interested in the visual arts or design should consider CMSC11111 Creative Coding. This course introduces the principles and practice of computer security. Inclusive Technology: Designing for Underserved and Marginalized Populations. Machine Learning in Medicine. Prerequisite(s): CMSC 15400 Equivalent Course(s): MAAD 20900. Teaching staff: Lang Yu
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