The course will involve a substantial programming project implementing a parallel computations. Email policy: We will prioritize answering questions posted to Piazza, notindividual emails. - Financial Math at UChicago literally . In this class we will engineer electronics onto Printed Circuit Boards (PCBs). Discrete Mathematics. The courses will take students through the whole data science lifecycle, with all the concepts that they need to know: data collection, data engineering, programming, statistical inference, machine learning, databases, and issues around ethics, privacy and algorithmic transparency, Nicolae said. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. BS students also take three courses in an approved related field outside computer science. This can lead to severe trustworthiness issues in ML. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. 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. CMSC22200. Computer Architecture. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. Equivalent Course(s): MATH 27800. This course covers computational methods for structuring and analyzing data to facilitate decision-making. This is a project-oriented course in which students are required to develop software in C on a UNIX environment. Senior at UChicago with interests in quantum computing, machine learning, mathematics, computer science, physics, and philosophy. Matlab, Python, Julia, R). (Links to an external site.) This course will introduce fundamental concepts in natural language processing (NLP). To do so, students must take three courses from an approved list in lieu of three major electives. C+: 77% or higher relationship between worldmaking and technology through social, political, and technical lenses. This concise review of linear algebra summarizes some of the background needed for the course. 100 Units. CMSC25422. Prerequisite(s): CMSC 27100, or MATH 20400 or higher. Mathematical Foundations of Machine Learning. . The Major Adviser maintains a website with up-to-date program details at majors.cs.uchicago.edu. This course provides an introduction to the concepts of parallel programming, with an emphasis on programming multicore processors. Request form available online https://masters.cs.uchicago.edu Equivalent Course(s): MPCS 51250. A Pass grade is given only for work of C- quality or higher. Winter This course is an introduction to programming, using exercises in graphic design and digital art to motivate and employ basic tools of computation (such as variables, conditional logic, and procedural abstraction). Prerequisite(s): CMSC 15100, CMSC 16100, CMSC 12100, or CMSC 10500. Basic processes of numerical computation are examined from both an experimental and theoretical point of view. Semantic Scholar's Logo. 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. 100 Units. 100 Units. CMSC25440. CMSC28510. 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. Gaussian mixture models and Expectation Maximization No courses in the minor can be double counted with the student's major(s) or with other minors, nor can they be counted toward general education requirements. We will cover algorithms for transforming and matching data; hypothesis testing and statistical validation; and bias and error in real-world datasets. Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms. B-: 80% or higher A core theme of the course is "generalization"; ensuring that the insights gleaned from data are predictive of future phenomena. Prerequisite(s): CMSC 15400 or CMSC 22000. Topics will include, among others, software specifications, software design, software architecture, software testing, software reliability, and software maintenance. Title: Mathematical Foundations of Machine Learning, Teaching Assistant(s): Takintayo Akinbiyi and Bumeng Zhuo, ClassSchedule: Sec 01: MW 3:00 PM4:20 PM in Ryerson 251 Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. CMSC14400. ), Zhuokai: Mondays 11am to 12pm, Location TBD. "The urgency with which businesses need strong data science talent is rapidly increasing, said Kjersten Moody, AB98 and chief data officer at Prudential Financial. Waitlist: We will not be accepting auditors this quarter due to high demand. Terms Offered: Spring This course is a direct continuation of CMSC 14100. Basic counting is a recurring theme and provides the most important source for sequences, which is another recurring theme. 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. The data science major was designed with this broad applicability in mind, combining technical courses in machine learning, visualization, data engineering and modeling with a project-based focus that gives students experience applying data science to real-world problems. Graduate courses and seminars offered by the Department of Computer Science are open to College students with consent of the instructor and department counselor. 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 lab section guides students through the implementation of a relational database management system, allowing students to see topics such as physical data organization and DBMS architecture in practice, and exercise general skills such as software systems development. Recently, The High Commissioner for Human Rights called for states to place moratoriums on AI until it is compliant with human rights. 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. Instructor(s): S. Kurtz (Winter), J. Simon (Autumn)Terms Offered: Autumn 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, directed acyclic graphs, and tournaments. Mathematical Foundations of Machine Learning Understand the principles of linear algebra and calculus, which are key mathematical concepts in machine learning and data analytics. 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. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. More events. Computers for Learning. In addition, we will discuss advanced topics regarding recent research and trends. Creative Machines and Innovative Instrumentation. Topics include programming with sockets; concurrent programming; data link layer (Ethernet, packet switching, etc. Note(s): This course is offered in alternate years. Note(s): anti-requisites: CMSC 25900, DATA 25900. The textbooks will be supplemented with additional notes and readings. 100 Units. Introductory Sequence (four courses required): Students who major in computer science must complete the introductory sequence: Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam are required to take an additional course from the list of courses approved for the Programming Languages and Systems Sequence, increasing the total number of courses required in the Programming Languages and Systems category from two to three. 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. Studied mathematical principles of machine learning (ML) via tutorial modules on Microsoft. A broad background on probability and statistical methodology will be provided. CMSC21800. Equivalent Course(s): MPCS 51250. 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. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. This course focuses on one intersection of technology and learning: computer games. Terms Offered: Spring This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Mathematical Foundations. D: 50% or higher Furthermore, the course will examine how memory is organized and structured in a modern machine. 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. This course includes a project where students will have to formulate hypotheses about a large dataset, develop statistical models to test those hypotheses, implement a prototype that performs an initial exploration of the data, and a final system to process the entire dataset. Thanks to the fantastic effort of many talented developers, these are easy to use and require only a superficial familiarity . Equivalent Course(s): MAAD 25300. This course will present a practical, hands-on approach to the field of bioinformatics. Prerequisite(s): CMSC 27100 or CMSC 27130, or MATH 15900 or MATH 19900 or MATH 25500; experience with mathematical proofs. Prerequisite(s): One of CMSC 23200, CMSC 23210, CMSC 25900, CMSC 28400, CMSC 33210, CMSC 33250, or CMSC 33251 recommended, but not required. 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. No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. Generally offered alternate years. And Department counselor Zhuokai: Mondays 11am to 12pm, Location TBD about being inquisitive asking... 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