Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. This class describes mathematical and perceptual principles, methods, and applications of "data visualization" (as it is popularly understood to refer primarily to tabulated data). This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. Others serve supporting roles, such as part-of-speech tagging and syntactic parsing. At what level does an entering student begin studying computer science at the University of Chicago? Machine Learning in Medicine. 100 Units. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Instructor(s): S. Kurtz (Winter), J. Simon (Autumn)Terms Offered: Autumn Students will design and implement systems that are reliable, capable of handling huge amounts of data, and utilize best practices in interface and usability design to accomplish common bioinformatics problems. Actuated User Interfaces and Technology. She joined the CSU faculty in 2013 after obtaining dual B.S. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. Prerequisite(s): CMSC 15200 or CMSC 16200. Advanced Algorithms. This introduction to quantum computing will cover the key principles of quantum information science and how they relate to quantum computing as well as the notation and operations used in QIS. Programming projects will be in C and C++. This course deals with numerical linear algebra, approximation of functions, approximate integration and differentiation, Fourier transformation, solution of nonlinear equations, and the approximate solution of initial value problems for ordinary differential equations. C+: 77% or higher This course is an introduction to "big" data engineering where students will receive hands-on experience building and deploying realistic data-intensive systems. CMSC29512may not be used for minor credit. 100 Units. This course is an introduction to formal tools and techniques which can be used to better understand linguistic phenomena. B-: 80% or higher 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 The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. All students will be evaluated by regular homework assignments, quizzes, and exams. 100 Units. About this Course. CMSC23400. From linear algebra and multivariate Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms. Note(s): This course meets the general education requirement in the mathematical sciences. Summer It describes several important modern algorithms, provides the theoretical . Discover how artificial intelligence (AI) and machine learning are revolutionizing how society operates and learn how to incorporate them into your businesstoday. 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. The course will combine analysis and discussion of these approaches with training in the programming and mathematical foundations necessary to put these methods into practice. However, building and using these systems pose a number of more fundamental challenges: How do we keep the system operating correctly even when individual machines fail? CMSC23010. 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. It aims to teach how to model threats to computer systems and how to think like a potential attacker. Generally offered alternate years. Random forests, bagging Emergent Interface Technologies. F: less than 50%. Final: Wednesday, March 13, 6-8pm in KPTC 120. 100 Units. Note 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. 100 Units. We will explore these concepts with real-world problems from different domains. 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. Is algorithmic bias avoidable? Undergraduate Computational Linguistics. Modern machine learning techniques have ushered in a new era of computing. Instructor(s): H. GunawiTerms Offered: Autumn The course will involve a substantial programming project implementing a parallel computations. In recent offerings, students have written a course search engine and a system to do speaker identification. Mathematical Foundations of Option Pricing . United States Students will gain further fluency with debugging tools and build systems. Prerequisite(s): CMSC 15400 Director, Machine Learning Engineer Bain & Company Frankfurt, Hesse, Germany 5 days ago Be among the first 25 applicants The course project will revolve around the implementation of a mini x86 operating system kernel. Directly from the pages of the book: While machine learning has seen many success stories, and software is readily available to design and train rich and flexible machine learning systems, we believe that the mathematical foundations of machine learning are important in order to understand fundamental principles upon which more complicated machine learning systems are built. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction Introduction to Optimization. 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. Chicago, IL 60637 Topics include automata theory, regular languages, context-free languages, and Turing machines. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. Knowledge of linear algebra and statistics is not assumed. 100 Units. Students may petition to have graduate courses count towards their specialization via this same page. We emphasize mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Labs expose students to software and hardware capabilities of mobile computing systems, and develop the capability to envision radical new applications for a large-scale course project. Most of the skills required for this process have nothing to do with one's technical capacity. These tools have two main uses. This course will not be offered again. Students can find more information about this course at http://bit.ly/cmsc12100-aut-20. Foundations of Machine Learning. As intelligent systems become pervasive, safeguarding their trustworthiness is critical. Students do reading and research in an area of computer science under the guidance of a faculty member. The following specializations are currently available: Computer Security:CMSC23200 Introduction to Computer Security 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. A core theme of the course is "scale," and we will discuss the theory and the practice of programming with large external datasets that cannot fit in main memory on a single machine. This sequence can be in the natural sciences, social sciences, or humanities and sequences in which earlier courses are prerequisites for advanced ones are encouraged. Instructor(s): S. KurtzTerms Offered: Spring No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. 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. 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). 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. There are three different paths to a, Digital Studies of Language, Culture, and History, History, Philosophy, and Social Studies of Science and Medicine, General Education Sequences for Science Majors, Elementary Functions and Calculus I-II (or higher), Engineering Interactive Electronics onto Printed Circuit Boards. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. 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. D: 50% or higher Does human review of algorithm sufficient, and in what cases? 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+). 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. Terms Offered: Spring CMSC12300. CMSC28000. Machine Learning - Python Programming. Prerequisite(s): MATH 25400 or 25700; open to students who are majoring in computer science who have taken CMSC 15400 along with MATH 16300 or MATH 16310 or Math 15910 or MATH 15900 or MATH 19900 Honors Introduction to Computer Science II. The class will rigorously build up the two pillars of modern . Students can earn a BA or BS degree with honors by attaining a grade of B or higher in all courses in the major and a grade of B or higher in three approved graduate computer science courses (30000-level and above). This course covers design and analysis of efficient algorithms, with emphasis on ideas rather than on implementation. Instructor: Yuxin Chen . Note: Students may petition to have graduate courses count towards their specialization. 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). No prior experience in security, privacy, or HCI is required. The course relies on a good math background, as can be expected from a CS PhD student. Two new projects will test out ways to make "intelligent" water [] We will introduce core security and privacy technologies, as well as HCI techniques for conducting robust user studies. Now, I have the background to better comprehend how data is collected, analyzed and interpreted in any given scientific article.. We will have several 3D printers available for use during the class and students will design and fabricate several parts during the course. Covering a story? 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.. This is a graduate-level CS course with the main target audience being TTIC PhD students (for which it is required) and other CS, statistics, CAM and math PhD students with an interest in machine learning. Instructor(s): Staff Prerequisite(s): CMSC 15400. Winter 100 Units. The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. One central component of the program was formalizing basic questions in developing areas of practice and gaining fundamental insights into these. Further topics include proof by induction; recurrences and Fibonacci numbers; graph theory and trees; number theory, congruences, and Fermat's little theorem; counting, factorials, and binomial coefficients; combinatorial probability; random variables, expected value, and variance; and limits of sequences, asymptotic equality, and rates of growth. In this course we will study the how machine learning is used in biomedical research and in healthcare delivery. Winter Equivalent Course(s): CMSC 30370, MAAD 20370. Introduction to Computer Vision. 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. Introduction to Computer Science I. Since joining the Gene Hackersa student group interested in synthetic biology and genomicsshe has developed an interest in coding, modeling and quantitative methods. Machine learning topics include thelasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks,and deep learning. Note(s): A more detailed course description should be available later. Prerequisite(s): CMSC 12100 7750: Mathematical Foundations of Machine Learning (Fall 2022) Description: This course for beginning graduate students develops the mathematical foundations of machine learning, rigorously introducing students to modeling and representation, statistical inference, and optimization. Introduction to Computer Science I. 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. Equivalent Course(s): CMSC 33710. Dependent types. In this class you will: (1) learn about these new developments during the lectures, (2) read HCI papers and summarize these in short weekly assignments, and lastly, (3) start inventing the future of computing interfaces by proposing a new idea in the form of a paper abstract, which you will present at the end of the semester and have it peer-reviewed in class by your classmates. 100 Units. Prospective minors should arrange to meet the departmental counselor for the minor no later than May 1 of their third year. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. Prerequisite(s): CMSC 12200 or CMSC 15200 or CMSC 16200, and the equivalent of two quarters of calculus (MATH 13200 or higher). CMSC25400. Students may not take CMSC 25910 if they have taken CMSC 25900 or DATA 25900. CMSC16100-16200. 100 Units. We will introduce the machine learning methods as we go, but previous familiarity with machine learning will be helpful. Instructor(s): B. SotomayorTerms Offered: Winter 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. In recent years, large distributed systems have taken a prominent role not just in scientific inquiry, but also in our daily lives. 100 Units. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Students will gain experience applying neural networks to modern problems in computer vision, natural language processing, and reinforcement learning. In addition, you will learn how to be mindful of working with populations that can easily be exploited and how to think creatively of inclusive technology solutions. Collaboration both within and across teams will be essential to the success of the project. Model selection, cross-validation Email policy: We will prioritize answering questions posted to Piazza, notindividual emails. 100 Units. NLP includes a range of research problems that involve computing with natural language. Note(s): Prerequisites: CMSC 15400 or equivalent, or graduate student. Becca: Wednesdays 10:30-11:30AM, JCL 257, starting week of Oct. 7. The curriculum includes the lambda calculus, type systems, formal semantics, logic and proof, and, time permitting, a light introduction to machine assisted formal reasoning. ), Course Website: https://willett.psd.uchicago.edu/teaching/fall-2019-mathematical-foundations-of-machine-learning/, Ruoxi (Roxie) Jiang (Head TA), Lang Yu, Zhuokai Zhao, Yuhao Zhou, Takintayo (Tayo) Akinbiyi, Bumeng Zhuo. Computer Science with Applications II. 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. This field is for validation purposes and should be left unchanged. Honors Introduction to Computer Science I-II. discriminatory, and is the algorithm the right place to look? 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 . Suite 222 Systems Programming I. In the modern world, individuals' activities are tracked, surveilled, and computationally modeled to both beneficial and problematic ends. 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. Understanding . The only opportunity students will have to complete the retired introductory sequence is as follows: Students who are not able to complete the retired introductory sequence on this schedule should contact the Director of Undergraduate Studies for Computer Science or the Computer Science Major Adviser for guidance. Digital fabrication involves translation of a digital design into a physical object. Programming assignments will be in python and we will use Google Collaboratory and Amazon AWS for compute intensive training. Quizzes will be via canvas and cover material from the past few lectures. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the CMSC28515. This course will take the first steps towards developing a human rights-based approach for analyzing algorithms and AI. 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