Cambridge University Press, 2020. Note(s): If an undergraduate takes this course as CMSC 29512, it may not be used for CS major or minor credit. towards the Machine Learning specialization, and, more Instead, we aim to provide the necessary mathematical skills to read those other books. 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. Furthermore, the course will examine how memory is organized and structured in a modern machine. 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. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. These courses may be courses taken for the major or as electives. This course also includes hands-on labs, where students will enhance their learning by implementing a modern microprocessor in a C simulator. For up-to-date information on our course offerings, please consult course-info.cs.uchicago.edu. Students may petition to take more advanced courses to fulfill this requirement. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. Youshould make the request for Pass/Fail grading in writing (private note on Piazza). . Application: electronic health record analysis, Professor of Statistics and Computer Science, University of Chicago, Auto-differentiable Ensemble Kalman Filters, Pure exploration in kernel and neural bandits, Mathematical Foundations of Machine Learning (Fall 2021), https://piazza.com/uchicago/fall2019/cmsc2530035300stat27700/home, https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/. Class discussion will also be a key part of the student experience. Note CMSC28510. CMSC22100. Instructor(s): R. StevensTerms Offered: TBD 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. This course covers education theory, psychology (e.g., motivation, engagement), and game design so that students can design and build an educational learning application. CMSC13600. This course covers the basics of computer systems from a programmer's perspective. Prerequisites: Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Topics include: Processes and threads, shared memory, message passing, direct-memory access (DMA), hardware mechanisms for parallel computing, synchronization and communication, patterns of parallel programming. 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.. 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. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. Winter 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. In addition, we will discuss advanced topics regarding recent research and trends. 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. CMSC25025. Features and models Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Decision trees There are roughly weekly homework assignments (about 8 total). 100 Units. SAND Lab spans research topics in security, machine learning, networked systems, HCI, data mining and modeling. Algorithmic questions include sorting and searching, discrete optimization, algorithmic graph theory, algorithmic number theory, and cryptography. Honors Theory of Algorithms. We also discuss the Gdel completeness theorem, the compactness theorem, and applications of compactness to algebraic problems. This course is an introduction to topics at the intersection of computation and language. ); end-to-end protocols (UDP, TCP); and other commonly used network protocols and techniques. Practical exercises in writing language transformers reinforce the the theory. Introduction to Computer Graphics. Instructor(s): Blase UrTerms Offered: Autumn D: 50% or higher Please sign up for the waitlist (https://waitlist.cs.uchicago.edu/) if you are looking for a spot. Students are encouraged, but not required, to fulfill this requirement with a physics sequence. 100 Units. The class provides a range of basic engineering techniques to allow students to develop their own actuated user interface systems, including 3D mechanical design, digital fabrication (e.g. 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, BA: Any sequence or pair of courses that fulfills the general education requirement in the physical sciences, BS: Any two-quarter sequence that fulfills the general education requirement in the physical sciences for science majors, Programming Languages and Systems Sequence (two courses from the list below), Theory Sequence (three courses from the list below), Five electives numbered CMSC 20000 or above, BS (three courses in an approved program in a related field), Students who entered the College prior to Autumn Quarter 2022 and have already completed, CMSC 15200 will be offered in Autumn Quarter 2022, CMSC 15400 will be offered in Autumn Quarter 2022 and Winter Quarter 2023, increasing the total number of courses required in this category from two to three, for a total of six electives, as well as the, taken to fulfill the programming languages and systems requirements, Outstanding undergraduates may apply to complete an MS in computer science along with a BA or BS (generalized to "Bx") during their four years at the College. This course explores new technologies driving mobile computing and their implications for systems and society. CMSC28400. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. Creative Machines and Innovative Instrumentation. 100 Units. The math subject is: Image created by Author Six math subjects become the foundation for machine learning. Instructor(s): ChongTerms Offered: Spring Note(s): This course is offered in alternate years. (0) 2022.11.13: Computer Vision: (0) 2022.11.13: Machine Learning with Python - Clustering (0) 2022.10.07 This course provides an introduction to basic Operating System principles and concepts that form as fundamental building blocks for many modern systems from personal devices to Internet-scale services. 100 Units. 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. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. Note(s): Students can use at most one of CMSC 25500 and TTIC 31230 towards a CS major or CS minor. CMSC23300. Introduction to Computer Science I-II. This course is cross-listed between CS, ECE, and . 100 Units. This course will not be offered again. Note(s): This is a directed course in mathematical topics and techniques that is a prerequisite for courses such as CMSC 27200 and 27400. Machine Learning. CMSC23400. Students will program in Python and do a quarter-long programming project. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. Inventing, Engineering and Understanding Interactive Devices. Request form available online https://masters.cs.uchicago.edu Instead of following an explicitly provided set of instructions, computers can now learn from data and subsequently make predictions. Vectors and matrices in machine learning models 100 Units. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. Collaboration both within and across teams will be essential to the success of the project. Prerequisite(s): MPCS 51036 or 51040 or 51042 or 51046 or 51100 This course is the second in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. Numerical Methods. Note(s): Necessary mathematical concepts will be presented in class. CMSC23200. Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. 100 Units. CMSC 29700. In this course, we will explore the use of proof assistants, computer programs that allow us to write, automate, and mechanically check proofs. Equivalent Course(s): LING 21010, LING 31010, CMSC 31010. The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. CMSC22600. Students will gain experience applying neural networks to modern problems in computer vision, natural language processing, and reinforcement learning. 100 Units. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself. Prerequisite(s): CMSC 23500. Instructor(s): S. KurtzTerms Offered: Spring 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. mathematical foundations of machine learning uchicago. 100 Units. (A full-quarter course is 100 units, with courses that take place in the first-half or second-half of the quarter being 50 units.) The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. Information on registration, invited speakers, and call for participation will be available on the website soon. No prior experience in security, privacy, or HCI is required. Students may substitute upper-level or graduate courses in similar topics for those on the list that follows with the approval of the departmental counselor. The textbooks will be supplemented with additional notes and readings. 100 Units. 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+). Exams: 40%. 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. Terms Offered: Winter The book is available at published by Cambridge University Press (published April 2020). . The course discusses both the empirical aspects of software engineering and the underlying theory. Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. The rst half of the book develops Boolean type theory | a type-theoretic formal foundation for mathematics designed speci cally for this course. Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT. The focus is on the mathematically-sound exposition of the methodological tools (in particular linear operators, non-linear approximation, convex optimization, optimal transport) and how they can be mapped to efficient computational algorithms. Introduction to Computer Science I. Labs focus on developing expertise in technology, and readings supplement lecture discussions on the human components of education. The course will place fundamental security and privacy concepts in the context of past and ongoing legal, regulatory, and policy developments, including: consumer privacy, censorship, platform content moderation, data breaches, net neutrality, government surveillance, election security, vulnerability discovery and disclosure, and the fairness and accountability of automated decision making, including machine learning systems. Instructor(s): H. GunawiTerms Offered: Autumn Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. This course is the first in a pair of courses designed to teach students about systems programming. Note(s): This course meets the general education requirement in the mathematical sciences. CMSC27502. CMSC11000. Students will also gain further fluency in working with the Linux command-line, including some basic operating system concepts, as well as the use of version control systems for collaborative software development. They also allow us to formalize mathematics, stating and proving mathematical theorems in a manner that leaves no doubt as to their meaning or veracity. This course is the first of a pair of courses that are designed to introduce students to computer science and will help them build computational skills, such as abstraction and decomposition, and will cover basic algorithms and data structures. $85.00 Hardcover. Homework and quiz policy: Your lowest quiz score and your lowest homework score will not be counted towards your final grade. This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Honors Introduction to Computer Science I. AI & Machine Learning Foundations and applications of computer algorithms making data-centric models, predictions, and decisions Modern machine learning techniques have ushered in a new era of computing. 100 Units. CMSC 25025-1: Machine Learning and Large-Scale Data Analysis (Amit) CMSC 25300-1: Mathematical Foundations of Machine Learning (Jonas) CMSC 25910-1: Engineering for Ethics, Privacy, and Fairness in Computer Systems (Ur) CMSC 27200-1: Theory of Algorithms (Orecchia) [Theory B] CMSC 27200-2: Theory of Algorithms (Orecchia) [Theory B] Quizzes will be via canvas and cover material from the past few lectures. Errata ( printing 1 ). Terms Offered: Winter Two new projects will test out ways to make "intelligent" water [] A written report is . 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.. Prerequisite(s): CMSC 25300 or CMSC 25400, knowledge of linear algebra. UChicago (9) iversity (9) SAS Institute (9) . Computer Architecture for Scientists. 100 Units. Basic apprehension of calculus and linear algebra is essential. CMSC23320. Creative Coding. Marti Gendel, a rising fourth-year, has used data science to support her major in biology. High-throughput automated biological experiments require advanced algorithms, implemented in high-performance computing systems, to interpret their results. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. The Department of Computer Science offers a seven-course minor: an introductory sequence of four courses followed by three approved upper-level courses. Instructor(s): Sarah SeboTerms Offered: Winter Based on this exam, students may place into: Both the BA and BS in computer science require fulfillment of the general education requirement in the mathematical sciences by completing an approved two-quarter calculus sequence. This course focuses on one intersection of technology and learning: computer games. Over time, technology has occupied an increasing role in education, with mixed results. A-: 90% or higher In this class we will engineer electronics onto Printed Circuit Boards (PCBs). The fourth Midwest Machine Learning Symposium (MMLS 2023) will take place on May 16-17, 2023 at UIC in Chicago, IL. 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. Honors Introduction to Computer Science I-II. Application: Handwritten digit classification, Stochastic Gradient Descent (SGD) 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. Winter This course covers principles of modern compiler design and implementation. The following specializations are available starting in Autumn 2019: Computer Security: CMSC 23200 Introduction to Computer Security and two courses from this list, Computer Systems: three courses from this list, over and above those taken to fulfill the programming languages and systems requirement, Data Science: CMSC 21800 Data Science for Computer Scientists and two courses from this list, Human Computer Interaction: CMSC 20300 Introduction to Human-Computer Interation and two courses from this list. Machine Learning - Python Programming. Discrete Mathematics. CMSC25610. Prerequisite(s): CMSC 15400 or CMSC 22000 Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110, or by consent. At the intersection of these two uses lies mechanized computer science, involving proofs about data structures, algorithms, programming languages and verification itself. This first course of the two would . 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. Appropriate for graduate students or advanced undergraduates. Prerequisite(s): MATH 25400 or MATH 25700 or (CMSC 15400 and (MATH 15910 or MATH 15900 or MATH 19900 or MATH 16300)) 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. 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.. The core theme for the Entrepreneurship in Technology course is that computer science students need exposure to the broad challenges of capturing opportunities and creating companies. 100 Units. AI approaches hold promise for improving models of climate and the universe, transforming waste products into energy sources, detecting new particles at the Large Hadron Collider, and countless . As such it has been a fertile ground for new statistical and algorithmic developments. 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. Equivalent Course(s): LING 28610. CMSC22200. 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. Linear classifiers 100 Units. Prerequisite(s): CMSC 11900 or CMSC 12300 or CMSC 21800 or CMSC 23710 or CMSC 23900 or CMSC 25025 or CMSC 25300. Networks and Distributed Systems. )" Skip to search form Skip to main content Skip to account menu. Although this course is designed to be at the level of mathematical sciences courses in the Core, with little background required, we expect the students to develop computational skills that will allow them to analyze data. Verification techniques to evaluate the correctness of quantum software and hardware will also be explored. Solely based on the Online Introduction to Computer Science Exam students may be placed into: Students who place into CMSC 14200 will receive credit for CMSC14100 Introduction to Computer Science I upon successfully completing CMSC14200 Introduction to Computer Science II. It will explore network design principles, spanning multilayer perceptrons, convolutional and recurrent architectures, attention, memory, and generative adversarial networks. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction Machine Learning for Computer Systems. 2017 The University of Chicago
Instead, C is developed as a part of a larger programming toolkit that includes the shell (specifically ksh), shell programming, and standard Unix utilities (including awk).
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