Mathematical topics covered include linear equations, regression, regularization,the singular value decomposition, and iterative algorithms. This course will introduce fundamental concepts in natural language processing (NLP). Download (official online versions from MIT Press): book ( PDF, HTML ). Note(s): This course meets the general education requirement in the mathematical sciences. Mobile Computing. Prerequisite(s): CMSC 12100, 15100, or 16100, and CMSC 15200, 16200, or 12300. Letter grades will be assigned using the following hard cutoffs: A: 93% or higher Both courses in this sequence meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15200 or 16200 to meet requirements for the major. 2. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. Summer Terms Offered: Winter Applications: image deblurring, compressed sensing, Weeks 5-6: Beyond Least Squares: Alternate Loss Functions, Hinge loss Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Equivalent Course(s): CMSC 33710. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. CMSC20300. In this course we will cover the foundations of 3D object design including computational geometry, the type of models that can and can't be fabricated, the uses and applications of digital fabrication, the algorithms, methods and tools for conversion of 3D models to representations that can be directly manufactured using computer controlled machines, the concepts and technology used in additive manufacturing (aka 3D printing) and the research and practical challenges of developing self-replicating machines. Formal constructive mathematics. We will introduce core security and privacy technologies, as well as HCI techniques for conducting robust user studies. Programming projects will be in C and C++. A small number of courses, such as CMSC29512 Entrepreneurship in Technology, may be used as College electives, but not as major electives. *Students interested in theory or machine learning can replace CMSC14300 Systems Programming I and CMSC14400 Systems Programming II with 20000-level electives in those fields. Introduction to Bioinformatics. CMSC15100-15200. 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. CMSC22880. Equivalent Course(s): CMSC 30280, MAAD 20380. 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.. Mathematical Foundations of Machine Learning - linear algebra (0) 2022.12.24: How does AI calculate the percentage in binary language system? Students are required to complete both written assignments and programming projects using OpenGL. Prerequisite(s): CMSC 15400. Prerequisite(s): PHYS 12200 or PHYS 13200 or PHYS 14200; or CMSC 12100 or CMSC 12200 or CMSC 12300; or consent of instructor. Instructor(s): Sarah SeboTerms Offered: Winter Topics include shortest paths, spanning trees, counting techniques, matchings, Hamiltonian cycles, chromatic number, extremal graph theory, Turan's theorem, planarity, Menger's theorem, the max-flow/min-cut theorem, Ramsey theory, directed graphs, strongly connected components, directly acyclic graphs, and tournaments. 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. Instructor(s): Staff Note: Students may petition to have graduate courses count towards their specialization. Operating Systems. Microsoft. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. More than half of the requirements for the minor must be met by registering for courses bearing University of Chicago course numbers. 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. Equivalent Course(s): CMSC 33230. In addition to his research, Veitch will teach courses on causality and machine learning as part of the new data science initiative at UChicago. 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. Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. 100 Units. Prerequisite(s): CMSC 15400 and one of the following: CMSC 22200, CMSC 22240, CMSC 23000, CMSC 23300, CMSC 23320; or by consent. Now shes using her data science knowledge in a summer internship analyzing health care technology investment opportunities. Terms Offered: Spring Unsupervised learning and clustering and two other courses from this list, CMSC20370 Inclusive Technology: Designing for Underserved and Marginalized Populations, CMSC23220 Inventing, Engineering and Understanding Interactive Devices, CMSC23240 Emergent Interface Technologies, Bachelors thesis in human computer interaction, approved as such, Machine Learning: three courses from this list, CMSC25040 Introduction to Computer Vision, Bachelors thesis in machine learning, approved as such, Programming Languages: three courses from this list, over and above those coursestaken to fulfill the programming languages and systems requirements, CMSC22600 Compilers for Computer Languages, Bachelors thesis in programming languages, approved as such, Theory: three courses from this list, over and above those taken tofulfill the theory requirements, CMSC28000 Introduction to Formal Languages, CMSC28100 Introduction to Complexity Theory, CMSC28130 Honors Introduction to Complexity Theory, Bachelors thesis in theory, approved as such. 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. Equivalent Course(s): DATA 25422, DATA 35422, CMSC 35422. CMSC20900. Inventing, Engineering and Understanding Interactive Devices. 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. This is a rigorous mathematical course providing an analytic view of machine learning. CMSC23700. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Topics will include distribute databases, materialized views, multi-dimensional indexes, cloud-native architectures, data versioning, and concurrency-control protocols. 100 Units. We will cover algorithms for transforming and matching data; hypothesis testing and statistical validation; and bias and error in real-world datasets. Announcements: We use Canvas as a centralized resource management platform. Developing synergy between humans and artificial intelligence through a better understanding of human behavior and human interaction with AI. It will explore network design principles, spanning multilayer perceptrons, convolutional and recurrent architectures, attention, memory, and generative adversarial networks. Non-majors may use either course in this sequence to meet the general education requirement in the mathematical sciences; students who are majoring in Computer Science must use either CMSC 15100-15200 or 16100-16200 to meet requirements for the major. Prerequisite(s): CMSC 15100 or CMSC 16100, and CMSC 27100 or CMSC 27700 or MATH 27700, or by consent. CMSC28400. 100 Units. Fostering an inclusive environment where students from all backgrounds can achieve their highest potential. The course examines in detail topics in both supervised and unsupervised learning. The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. There is one approved general program for both the BA and BS degrees, comprised of introductory courses, a sequence in Theory, and a sequence in Programming Languages and Systems, followed by advanced electives. lecture slides . Methods of algorithm analysis include asymptotic notation, evaluation of recurrent inequalities, the concepts of polynomial-time algorithms, and NP-completeness. Engineering for Ethics, Privacy, and Fairness in Computer Systems. Letter grades will be assigned using the following hard cutoffs: A: 93% or higher Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Lecture hours: Tu/Th, 9:40-11am CT via Zoom (starting 03/30/2021); Please retrieve the Zoom meeting links on Canvas. Prerequisite(s): CMSC 12300 or CMSC 15400. Prerequisite(s): CMSC 15400 or equivalent, and instructor consent. The numerical methods studied in this course underlie the modeling and simulation of a huge range of physical and social phenomena, and are being put to increasing use to an increasing extent in industrial applications. Her experience in Introduction to Data Science not only showed her how to use these tools in her research, but also how to effectively evaluate how other scientists deploy data science, AI and other approaches. Requires TTIC31020as a prerequisite, and relies on a similar or slightly higher mathematical preparation. Prerequisite(s): CMSC 15400. The textbooks will be supplemented with additional notes and readings. Students may petition to take more advanced courses to fulfill this requirement. The Center for Data and Computing is an intellectual hub and incubator for data science and artificial intelligence research at the University of Chicago. Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms. Instructor(s): S. KurtzTerms Offered: Spring While this course should be of interest for students interested in biological sciences and biotechnology, techniques and approaches taught will be applicable to other fields. The courses provided Hitchings with technical skills in programming, data analytics, statistical prediction and visualization, and allowed her to exercise that new toolset on real-world problems. This can lead to severe trustworthiness issues in ML. Students will receive detailed feedback on their work from computer scientists, artists, and curators at the Museum of Science & Industry (MSI). Discrete Mathematics. Scalar first-order hyperbolic equations will be considered. Prerequisite(s): CMSC 15400 and knowledge of linear algebra, or by consent. Tivadar Danka. Note(s): This course is offered in alternate years. MIT Press, Second Edition, 2018. Entrepreneurship in Technology. Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. We also study some prominent applications of modern computer vision such as face recognition and object and scene classification. 100 Units. Understanding . (Links to an external site. Verification techniques to evaluate the correctness of quantum software and hardware will also be explored. Jointly with the School of the Art Institute of Chicago (SAIC), this course will examine privacy and security issues at the intersection of the physical and digital worlds. Machine Learning. 100 Units. The course will cover algorithms for symmetric-key and public-key encryption, authentication, digital signatures, hash functions, and other primitives. Instructor(s): S. Kurtz (Winter), J. Simon (Autumn)Terms Offered: Autumn Each of these mini projects will involve students programming real, physical robots interacting with the real world. 100 Units. This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. 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. Instructor consent required. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Learn more about the course offerings in the Foundations Year below: Foundations YearAutumn Quarter We concentrate on a few widely used methods in each area covered. 100 Units. 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. Defining this emerging field by advancing foundations and applications. CMSC21400. UChicago students will have a wide variety of opportunities to engage projects across different sectors, disciplines and domains, from problems drawn from environmental and human rights groups to AI-driven finance and industry to cutting-edge research problems from the university, our national labs and beyond. This course explores new technologies driving mobile computing and their implications for systems and society. 100 Units. 100 Units. Students will gain further fluency with debugging tools and build systems. Example topics include instruction set architecture (ISA), pipelining, memory hierarchies, input/output, and multi-core designs. CMSC27410. CMSC22001. Instructor(s): S. LuTerms Offered: Autumn Masters Program in Computer Science (MPCS), Masters in Computational Analysis and Public Policy (MSCAPP), Equity, Diversity, and Inclusion (EDI) Committee, SAND (Security, Algorithms, Networking and Data) Lab, Network Operations and Internet Security (NOISE) Lab, Strategic IntelliGence for Machine Agents (SIGMA) Lab. 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. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. Equivalent Course(s): CMSC 32900. 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