CMSC25900. The Lasso and proximal point algorithms Note(s): This course meets the general education requirement in the mathematical sciences. This course could be used a precursor to TTIC 31020, Introduction to Machine Learning or CSMC 35400. 100 Units. Collaboration both within and across teams will be essential to the success of the project. The textbooks will be supplemented with additional notes and readings. We will write code in JavaScript and related languages, and we will work with a variety of digital media, including vector graphics, raster images, animations, and web applications. Recently, The High Commissioner for Human Rights called for states to place moratoriums on AI until it is compliant with human rights. Computer Architecture for Scientists. Applications: bioinformatics, face recognition, Week 3: Singular Value Decomposition (Principal Component Analysis), Dimensionality reduction Security, Privacy, and Consumer Protection. The course is also intended for students outside computer science who are experienced with programming and computing with scientific data. Students may not use AP credit for computer science to meet minor requirements. Basic counting is a recurring theme and provides the most important source for sequences, which is another recurring theme. Exams (40%): Two exams (20% each). Introduction to Data Engineering. 100 Units. 100 Units. Students will learn both technical fundamentals and how to apply these concepts to public policy outputs and recommendations. We will cover algorithms for transforming and matching data; hypothesis testing and statistical validation; and bias and error in real-world datasets. Students must be admitted to the joint MS program. 100 Units. Random forests, bagging Instructor(s): William L Trimble / TBDTerms Offered: Spring Matlab, Python, Julia, or R). Letter grades will be assigned using the following hard cutoffs: A: 93% or higher Instructor(s): B. UrTerms Offered: Spring CMSC23230. STAT 30900 / CMSC 3781: Mathematical Computation I Matrix Computation, STAT 31015 / CMSC 37811: Mathematical Computation II Convex Optimization, STAT 37710 / CMSC 35400: Machine Learning, TTIC 31150/CMSC 31150: Mathematical Toolkit. . The course discusses both the empirical aspects of software engineering and the underlying theory. Computing Courses - 250 units. Terms Offered: Spring This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. Professor, Departments of Computer Science and Statistics, Assistant Professor, Department of Computer Science, Edward Carson Waller Distinguished Service Professor Emeritus, Departments of Computer Science and Linguistics, Frederick H. Rawson Distinguished Service Professor in Medicine and Computer Science, Assistant Professor, Department of Computer Science, College, Assistant Professor, Computer Science (starting Fall 2023), Associate Professor, Department of Computer Science, Associate Professor, Departments of Computer Science and Statistics, Associate Professor, Toyota Technological Institute, Professor, Toyota Technological Institute, Assistant Professor, Computer Science and Data Science, Assistant Professor, Toyota Technological Institute. Researchers explore the next generation of learning methods, including machine teaching, human-centered AI, and applications in language, image processing, and scientific discovery. Note(s): Students can use at most one of CMSC 25500 and TTIC 31230 towards a CS major or CS minor. Application: text classification, AdaBoost Prerequisite(s): CMSC 20300 CMSC23240. Through hands-on programming assignments and projects, students will design and implement computer systems that reflect both ethics and privacy by design. Autumn/Spring. Scientific visualization combines computer graphics, numerical methods, and mathematical models of the physical world to create a visual framework for understanding and solving scientific problems. 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. Reading and Research in Computer Science. This first course of the two would . 100 Units. 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. Scalar first-order hyperbolic equations will be considered. This course is the first in a pair of courses designed to teach students about systems programming. Prerequisite(s): MATH 25400 or MATH 25700 or (CMSC 15400 and (MATH 15910 or MATH 15900 or MATH 19900 or MATH 16300)) This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. In collaboration with others, you will complete a mini-project and a final project, which will involve the design and fabrication of a functional scientific instrument. 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. CMSC27502. This course covers the basics of computer systems from a programmer's perspective. UChicago Computer Science 25300/35300 and Applied Math 27700: Mathematical Foundations of Machine Learning, Fall 2019 UChicago STAT 31140: Computational Imaging Theory and Methods UChicago Computer Science 25300/35300 Mathematical Foundations of Machine Learning, Winter 2019 UW-Madison ECE 830 Estimation and Decision Theory, Spring 2017 The iterative nature of the design process will require an appreciable amount of time outside of class for completing projects. STAT 37400: Nonparametric Inference (Lafferty) Fall. Instructor(s): B. SotomayorTerms Offered: Winter Equivalent Course(s): STAT 37601. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Lecture 1: Intro -- Mathematical Foundations of Machine Learning She joined the CSU faculty in 2013 after obtaining dual B.S. This course is offered in the Pre-College Summer Immersion program. Unsupervised learning and clustering Prerequisite(s): CMSC 15400. Equivalent Course(s): MATH 28530. The course will involve a substantial programming project implementing a parallel computations. At the same time, the structure and evolution of networks is determined by the set of interactions in the domain. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. The Leibniz Institute SAFE is seeking to fill the position of a Research Assistant (m/f/d), 50% Position, salary group E13 TV-H. We are looking for a research assistant for the project "From Machine Learning to Machine Teaching (ML2MT) - Making Machines AND Humans Smarter" funded by Volkswagen Foundation with Prof. Pelizzon being one of . Equivalent Course(s): MAAD 13450, HMRT 23450. Students will partner with organizations on and beyond campus to advance research, industry projects and social impact through what they have learned, transcending the conventional classroom experience., The Colleges new data science major offers students a remarkable new interdisciplinary learning opportunity, said John W. Boyer, dean of the College. Equivalent Course(s): MATH 28000. | Learn more about Rohan Kumar's work experience, education . Instructor(s): R. StevensTerms Offered: TBD Formal constructive mathematics. Basic data structures, including lists, binary search trees, and tree balancing. This course will present a practical, hands-on approach to the field of bioinformatics. Professor Ritter is one of the best quants in the industry and he has a very unique and insightful way of approaching problems, these courses are a must. Any 20000-level computer science course taken as an elective beyond requirements for the major may, with consent of the instructor, be taken for P/F grading. 100 Units. Note(s): anti-requisites: CMSC 25900, DATA 25900. Late Policy: Late homework and quiz submissions will lose 10% of the available points per day late. 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. When she arrived at the University of Chicago, she was passionate about investigative journalism and behavioral economics, with a focus on narratives over number-crunching. Algorithms and artificial intelligence (AI) are a new source of global power, extending into nearly every aspect of life. Instructor(s): T. DupontTerms Offered: Autumn. The article is an analysis of the current topic - digitalization of the educational process. Prerequisite(s): CMSC 12300 or CMSC 15400. Computing systems have advanced rapidly and transformed every aspect of our lives for the last few decades, and innovations in computer architecture is a key enabler. (Links to an external site. Introduction to Computer Science II. After successfully completing this course, a student should have the necessary foundation to quickly gain expertise in any application-specific area of computer modeling. Prerequisite(s): CMSC 12300 or CMSC 15400, or MATH 15900 or MATH 25500. This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics, etc. 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. CMSC15400. Terms Offered: Winter The ideal student in this course would have a strong interest in the use of computer modeling as predictive tool in a range of discplines -- for example risk management, optimized engineering design, safety analysis, etc. Note 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. Email policy: The TAs and I will prioritize answering questions posted to Piazza, NOT individual emails. Scalable systems are needed to collect, stream, process, and validate data at scale. Students do reading and research in an area of computer science under the guidance of a faculty member. This course covers computational methods for structuring and analyzing data to facilitate decision-making. CMSC 29700. Honors Introduction to Complexity Theory. 100 Units. Prerequisite(s): MATH 27700 or equivalent Ph: 773-702-7891 Data Science for Computer Scientists. Instructor(s): A. ElmoreTerms Offered: Winter Introduction to Robotics. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models. This course will not be offered again. What makes an algorithm An introduction to the field of Human-Computer Interaction (HCI), with an emphasis in understanding, designing and programming user-facing software and hardware systems. ); internet and routing protocols (IP, IPv6, ARP, etc. The computer science program offers BA and BS degrees, as well as combined BA/MS and BS/MS degrees. Data types include images, archives of scientific articles, online ad clickthrough logs, and public records of the City of Chicago. Learning goals and course objectives. Theory Sequence (three courses required): Students must choose three courses from the following (one course each from areas A, B, and C). Prerequisite(s): Placement into MATH 15100 or completion of MATH 13100, or instructors consent, is a prerequisite for taking this course. C+: 77% or higher The statistical foundations of machine learning. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Mathematical Foundations. Data science is all about being inquisitive - asking new questions, making new discoveries, and learning new things. Machine Learning. The computer science minor must include three courses chosen from among all 20000-level CMSC courses and above. Prerequisite(s): CMSC 14300 or CMSC 15200. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Students can find more information about this course at http://bit.ly/cmsc12100-aut-20. Quantum Computer Systems. files that use the command-line version of DrScheme. Feature functions and nonlinear regression and classification 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. We are expanding upon the conventional view of data sciencea combination of statistics, computer science and domain expertiseto build out the foundations of the field, consider its ethical and societal implications and communicate its discoveries to make the most powerful and positive real-world impact.. Marti Gendel, a rising fourth-year, has used data science to support her major in biology. hold zoom meetings, where you can participate, ask questions directly to the instructor. Prerequisite(s): By consent of instructor and approval of department counselor. Most of the skills required for this process have nothing to do with one's technical capacity. With colleagues across the UChicago campus, the department also examines the considerable societal impacts and ethical questions of AI and machine learning, to ensure that the potential benefits of these approaches are not outweighed by their risks. Fax: 773-702-3562. for a total of six electives, as well as theadditional Programming Languages and Systems Sequence course mentioned above. 100 Units. Unsupervised learning and clustering Applications: recommender systems, PageRank, Ridge regression 100 Units. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Terms Offered: Winter This class covers the core concepts of HCI: affordances, mental models, selection techniques (pointing, touch, menus, text entry, widgets, etc), conducting user studies (psychophysics, basic statistics, etc), rapid prototyping (3D printing, etc), and the fundamentals of 3D interfaces (optics for VR, AR, etc). Residing in the middle of the system design layers, computer architecture interacts with both the software stack (e.g., operating systems and applications) and hardware technologies (e.g., logic gates, interconnects, and memories) to enable efficient computing with unprecedented capabilities. Data visualizations provide a visual setting in which to explore, understand, and explain datasets. CMSC23400. Scientific Visualization. This course explores new technologies driving mobile computing and their implications for systems and society. ), 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. Instructor(s): Sarah SeboTerms Offered: Winter Mobile Computing. We will explore analytic toolkits from science and technology studies (STS) and the philosophy of technology to probe the The present review "Genetic redundancy in rye shows in a variety of ways" by Vershinin et al., investigated the genomic organization of 19 rye chromosomes with a description of the molecular mechanisms contributing the evolution of genomic structure. Hardcover. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110, or by consent. Students who are interested in data science should consider starting with DATA11800 Introduction to Data Science I. Mathematical Foundations of Machine Learning. Outline: This course is an introduction to key mathematical concepts at the heart of machine learning. Rob Mitchum. Prerequisite(s): CMSC 27100 or CMSC 27130 or CMSC 37110 or consent of the instructor. CMSC14400. Introduction to Data Science I. 100 Units. Recent approaches have unlocked new capabilities across an expanse of applications, including computer graphics, computer vision, natural language processing, recommendation engines, speech recognition, and models for understanding complex biological, physical, and computational systems. Decision trees 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. The course examines in detail topics in both supervised and unsupervised learning. lecture slides . Prerequisite(s): A year of calculus (MATH 15300 or higher), a quarter of linear algebra (MATH 19620 or higher), and CMSC 10600 or higher; or consent of instructor. Terms Offered: Winter Programming Languages and Systems Sequence (two courses required): Students who place out of CMSC14300 Systems Programming I based on the Systems Programming Exam must replace it with an additional course from this list, Real-world examples, case-studies, and lessons-learned will be blended with fundamental concepts and principles. Instead of following an explicitly provided set of instructions, computers can now learn from data and subsequently make predictions. To earn a BA in computer science any sequence or pair of courses approved by the Physical Sciences Collegiate Division may be used to complete the general education requirement in the physical sciences. 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. Gaussian mixture models and Expectation Maximization What is ML, how is it related to other disciplines? CMSC20900. This course covers the basics of computer systems from a programmer's perspective. Prerequisite(s): CMSC 11900, CMSC 12200, CMSC 15200, or CMSC 16200. In recent offerings, students have written programs to simulate a model of housing segregation, determine the number of machines needed at a polling place, and analyze tweets from presidential debates. CMSC21010. Equivalent Course(s): MPCS 54233. 100 Units. The course will be fast moving and will involve weekly program assignments. Quizzes: 30%. . towards the Machine Learning specialization, and, more 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.. 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. Introduction to Bioinformatics. Note(s): Prior experience with basic linear algebra (matrix algebra) is recommended. Equivalent Course(s): CMSC 30370, MAAD 20370. Students may petition to take more advanced courses to fulfill this requirement. Foundations of Machine Learning. This course presented introductory techniques of problem solving, algorithm construction, program coding, and debugging, as interdisciplinary arts adaptable to a wide range of disciplines. Introduction to Robotics gives students a hands-on introduction to robot programming covering topics including sensing in real-world environments, sensory-motor control, state estimation, localization, forward/inverse kinematics, vision, and reinforcement learning. Design techniques include "divide-and-conquer" methods, dynamic programming, greedy algorithms, and graph search, as well as the design of efficient data structures. This course will cover the principles and practice of security, privacy, and consumer protection. Medical: 205-921-5556 Fax: 205-921-5595 2131 Military Street S Hamilton, AL 35570 used equipment trailers for sale near me B+: 87% or higher Prerequisite(s): CMSC 15400 or CMSC 22000. Machine Learning in Medicine. 773.702.8333, University of Chicago Data Science Courses 2022-2023. 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. Prerequisite(s): CMSC 15400 and some experience with 3D modeling concepts. The kinds of things you will learn may include mechanical design and machining, computer-aided design, rapid prototyping, circuitry, electrical measurement methods, and other techniques for resolving real-world design problems. 100 Units. Terms Offered: Spring Prerequisite(s): CMSC 20300 or CMSC 20600 or CMSC 21800 or CMSC 22000 or CMSC 22001 or CMSC 23000 or CMSC 23200 or CMSC 23300 or CMSC 23320 or CMSC 23400 or CMSC 23500 or CMSC 23900 or CMSC 25025. Find our class page at: https://piazza.com/uchicago/fall2019/cmsc2530035300stat27700/home(Links to an external site.) Team projects are assessed based on correctness, elegance, and quality of documentation. Now, I have the background to better comprehend how data is collected, analyzed and interpreted in any given scientific article.. All paths prepare students with the toolset they need to apply these skills in academia, industry, nonprofit organizations, and government. Dependent types. Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The department also offers a minor. Instructor(s): ChongTerms Offered: Spring It will also introduce algorithmic approaches to fairness, privacy, transparency, and explainability in machine learning systems. Students who are interested in the visual arts or design should consider CMSC11111 Creative Coding. How do we ensure that all the machines have a consistent view of the system's state? 100 Units. Notes 01, Introduction I. Vector spaces and linear representations Notes 02, first look at linear representations Notes 03, linear vector spaces Notes 04, norms and inner products 100 Units. CMSC28515. Learnt data science, learn its content, discipline construction, applications and employment prospects. CMSC29512may not be used for minor credit. Prerequisite(s): CMSC 11900 or 12200 or CMSC 15200 or CMSC 16200. 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. (Mathematical Foundations of Machine Learning) or equivalent (e.g. Some are user-facing applications, such as spam classification, question answering, summarization, and machine translation. Mathematical Foundations of Machine Learning. Topics include automata theory, regular languages, context-free languages, and Turing machines. This course emphasizes mathematical discovery and rigorous proof, which are illustrated on a refreshing variety of accessible and useful topics. Engineering Interactive Electronics onto Printed Circuit Boards. Matlab, Python, Julia, or R). Please refer to the Computer Science Department's websitefor an up-to-date list of courses that fulfill each specialization, including graduate courses. This course provides an introduction to the concepts of parallel programming, with an emphasis on programming multicore processors. CMSC15200. B: 83% or higher Prerequisite(s): CMSC 15200 or CMSC 16200. The textbooks will be supplemented with additional notes and readings. 100 Units. 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. His group developed mathematical models based on this data and then began using machine-learning methods to reveal new information about proteins' basic design rules. CMSC15100. All rights reserved. CMSC11111. CMSC12200. Mathematics (1) Mechanical Engineering (1) Photography (1) . CMSC20600. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation and communication of results. Foundations of Machine Learning. 100 Units. Basic mathematics for reasoning about programs, including induction, inductive definition, propositional logic, and proofs. Introduction to Numerical Partial Differential Equations. Introduction to Complexity Theory. 100 Units. 100 Units. The course covers both the foundations of 3D graphics (coordinate systems and transformations, lighting, texture mapping, and basic geometric algorithms and data structures), and the practice of real-time rendering using programmable shaders. 100 Units. Students can select data science as their primary program of study, or combine the interdisciplinary field with a second major. Mobile computing is pervasive and changing nearly every aspect of society. It will explore network design principles, spanning multilayer perceptrons, convolutional and recurrent architectures, attention, memory, and generative adversarial networks. CMSC12100. 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. Computer Networking Database Management Artificial Intelligence AWS Foundation Machine Learning Information Technology Data Analytics Software Development IoT Business Analytics Software Testing Oracle . The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. 100 Units. Topics include: algebraic datatypes, an elegant language for describing and manipulating domain-specific data; higher-order functions and type polymorphism, expressive mechanisms for abstracting programs; and a core set of type classes, with strong connections to category theory, that serve as a foundational and practical basis for mixing pure functions with stateful and interactive computations. Prerequisite(s): CMSC 25300, CMSC 25400, CMSC 25025, or TTIC 31020. Equivalent Course(s): LING 21010, LING 31010, CMSC 31010. Sec 02: MW 9:00 AM-10:20AM in Crerar Library 011, Textbook(s): Eldn,Matrix Methods in Data Mining and Pattern Recognition(recommended). This course is an introduction to database design and implementation. For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf. Honors Introduction to Computer Science I-II. Plan accordingly. Plan accordingly. No prior background in artificial intelligence, algorithms, or computer science is needed, although some familiarity with human-rights philosophy or practice may be helpful. CMSC16100-16200. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Features and models This course is an introduction to topics at the intersection of computation and language. Systems Programming I. Prerequisite(s): DATA 11800 , or STAT 11800 or CMSC 11800 or consent of instructor. CMSC27700. Students from 11 different majors, including all four collegiate divisions, have chosen a data science minor. Foundations and applications of computer algorithms making data-centric models, predictions, and decisions. 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 For instance . There are roughly weekly homework assignments (about 8 total). This course can be used towards fulfilling the Programming Languages and Systems requirement for the CS major. This course meets the general education requirement in the mathematical sciences. Data-driven models are revolutionizing science and industry. 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. Instructor(s): H. GunawiTerms Offered: Autumn More advanced topics on data privacy and ethics, reproducibility in science, data encryption, and basic machine learning will be introduced. 100 Units. Topics include machine language programming, exceptions, code optimization, performance measurement, system-level I/O, and concurrency. This course leverages human-computer interaction and the tools, techniques, and principles that guide research on people to introduce you to the concepts of inclusive technology design. Can participate, ask questions directly to the instructor computational thinking and skills to students who are in. Techniques for data analysis are used to illustrate both effective and fallacious uses of science. Theadditional programming Languages and systems sequence course mentioned above well as combined BA/MS and BS/MS degrees performance measurement, I/O... Applications: recommender systems, PageRank, Ridge regression 100 Units CMSC,! Visual setting in which to explore, understand, and validate data at scale or consent of instructor fax 773-702-3562.... Maximization What is ML, how is it related to other disciplines of scientific articles, ad. And proximal point algorithms note ( s ): R. StevensTerms Offered: TBD Formal constructive mathematics matrix algebra is... Records of the system 's state foundations and applications of computer systems that reflect both ethics and privacy by.... Implications for systems and society to do with one 's technical capacity:.. Languages, context-free Languages, context-free Languages, context-free Languages, context-free Languages context-free! Spam classification, question answering, summarization, and explain datasets engineering ( 1.... Propositional logic, and economics, etc constructive mathematics and analyzing data to facilitate decision-making a... Underlying theory 3D modeling concepts after successfully completing this course covers the of. 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