Team projects are assessed based on correctness, elegance, and quality of documentation. Techniques studied include the probabilistic method. Instructor(s): ChongTerms Offered: Spring Equivalent Course(s): MAAD 25300. Is algorithmic bias avoidable? 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. Multimedia Programming as an Interdisciplinary Art I. Prerequisite(s): MATH 15900 or MATH 25400, or CMSC 27100, or by consent. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. Loss, risk, generalization No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. Model selection, cross-validation The first phase of the course will involve prompts in which students design and program small-scale artworks in various contexts, including (1) data collected from web browsing; (2) mobility data; (3) data collected about consumers by major companies; and (4) raw sensor data. Terms Offered: Winter Prerequisite(s): CMSC 14300 or CMSC 15200. Systems Programming II. The course will involve a business plan, case-studies, and supplemental reading to provide students with significant insights into the resolve required to take an idea to market. The focus is on matrix methods and statistical models and features real-world applications ranging from classification and clustering to denoising and recommender systems. Starting AY 2022-23, students who have taken CMSC 16100 are not allowed to register for CMSC 22300. 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. 100 Units. Lecture 1: Intro -- Mathematical Foundations of Machine Learning 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. This course provides an introduction to the concepts of parallel programming, with an emphasis on programming multicore processors. A-: 90% or higher Homework exercises will give students hands-on experience with the methods on different types of data. Introduction to Human-Computer Interaction. In the field of machine learning and data science, a strong foundation in mathematics is essential for understanding and implementing advanced algorithms. Appropriate for graduate students or advanced undergraduates. 100 Units. While digital fabrication has been around for decades, only now has it become possible for individuals to take advantage of this technology through low cost 3D printers and open source tools for 3D design and modeling. 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. 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. Matlab, Python, Julia, or R). Equivalent Course(s): STAT 27700, CMSC 35300. The course culminates in the production and presentation of a capstone interactive artwork by teams of computer scientists and artists; successful products may be considered for prototyping at the MSI. The PDF will include all information unique to this page. 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. Introduction to Creative Coding. In this course we will study the how machine learning is used in biomedical research and in healthcare delivery. Its really inspiring that I can take part in a field thats rapidly evolving.. D: 50% or higher Cryptography is the use of algorithms to protect information from adversaries. 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. Terms Offered: Autumn Basic processes of numerical computation are examined from both an experimental and theoretical point of view. Design techniques include divide-and-conquer methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. We split the book into two parts: Mathematical foundations; Example machine learning algorithms that use the mathematical foundations Figure 4.1: An algorithmic framework for online strongly convex programming. UChicago (9) iversity (9) SAS Institute (9) . Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms. Data-driven models are revolutionizing science and industry. CMSC22600. 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. 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] Opportunities for PhDs to work on world-class computer science research with faculty members. Students may petition to have graduate courses count towards their specialization via this same page. Instructor(s): William Trimble / TBDTerms Offered: Autumn These courses may be courses taken for the major or as electives. Introduction to Computer Graphics. CMSC23220. Note(s): This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. This course covers the basics of computer systems from a programmer's perspective. Features and models Plan accordingly. We concentrate on a few widely used methods in each area covered. 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. CMSC22001. Prerequisite(s): CMSC 14200, or placement into CMSC 14300, is a prerequisite for taking this course. In addition, we will discuss advanced topics regarding recent research and trends. How does algorithmic decision-making impact democracy? Mathematical Foundations of Machine Learning. This course is the first in a pair of courses designed to teach students about systems programming. Practical exercises in writing language transformers reinforce the the theory. Instructor(s): A. RazborovTerms Offered: Autumn Other new courses in development will cover misinterpretation of data, the economic value of data and the mathematical foundations of machine learning and data science. Equivalent Course(s): MPCS 54233. Prerequisite(s): CMSC 20300 Recent papers in the field of Distributed Systems have described several solutions (such as MapReduce, BigTable, Dynamo, Cassandra, etc.) Prerequisite(s): CMSC 15400. An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. 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. Students are required to complete both written assignments and programming projects using OpenGL. Type a description and hit enter to create a bookmark; 3. Surveillance Aesthetics: Provocations About Privacy and Security in the Digital Age. hold zoom meetings, where you can participate, ask questions directly to the instructor. Now shes using her data science knowledge in a summer internship analyzing health care technology investment opportunities. Instructor(s): R. StevensTerms Offered: TBD Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. Parallel Computing. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. Introduction to Optimization. Rather than emailing questions to the teaching staff, we encourage you to post your questions on, We will not be accepting auditors this quarte. 100 Units. This course is the first in a three-quarter sequence that teaches computational thinking and skills to students in the sciences, mathematics, economics, etc. In this hands-on, practical course, you will design and build functional devices as a means to learn the systematic processes of engineering and fundamentals of design and construction. Helping someone suffering from schizophrenia determine reality; an alarm to help maintain distance during COVID; adding a fun gamification element to exercise. Hardcopy ( MIT Press, Amazon ). Logistic regression Prerequisite(s): CMSC 23300 or CMSC 23320 Prerequisite(s): CMSC 15400 Programming projects will be in C and C++. Experience with mathematical proofs. Use all three of the most important Python tensor libraries to manipulate tensors: NumPy, TensorFlow, and PyTorch are three Python libraries. Prerequisite(s): CMSC 15400 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 . Students hands-on experience with the methods on different types of data conceptual tools the! Experience with the methods on different types of data AY 2022-23, students have! Numerical computation are examined from both an experimental and theoretical point of view in the field of machine learning presented. 9 ) SAS Institute ( 9 ) can participate, ask questions directly to the instructor in biomedical and! 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