]> Course Recommender Individuals Ontology This ontology captures Individuals relevant to course recommendation, including a large selection of Courses, CourseSections, and some Students with PlanOfStudys. Jacob Shomstein Kelly Fellenzer Owen Xie Sola Shirai http://opensource.org/licenses/MIT 2020-11-28T23:59:59 Course Recommender Team Copyright (c) 2020 Kelly Fellenzer, Jacom Shomstein, Sola Shirai, and Owen Xie https://spec.edmcouncil.org/fibo/ontology/BE/ https://spec.edmcouncil.org/fibo/ontology/FND/ https://www.omg.org/spec/LCC/ https://raw.githubusercontent.com/tetherless-world/ontology-engineering/566ec334ec42070cb587f769021415d358c2424a/oe2020/course-recommender/course-recommender-individuals.rdf Schedules have been added for CourseSections. Labels have been added for CourseSections. Placeholder advisor name placeholder BS placeholder computer science major Rensselaer Polytechnic Institute globally recognized university situated on 275 acres in Troy NY, offering 746 labs, studios, and technology spaces used for research RPI Rensselaer https://rpi.edu/about/ Rensselaer Polytechnic Institute address physical street address for Rensselaer Polytechnic Institute 110 8th Street 12180-3522 Troy Rensselaer Polytechnic Institute as legal entity legal entity that is Rensselaer Polytechnic Institute ARTS COGS COMM ECON IHSS LANG LITR PHIL PSYC STSH STSS WRIT 2000 ARTS COMM IHSS LANG LITR PHIL STSH WRIT COGS ECON IHSS PSYC STSS ARTS COGS COMM ECON IHSS LANG LITR PHIL PSYC STSH STSS WRIT 4000 communication intensive ARTS COGS COMM ECON IHSS LANG LITR PHIL PSYC STSH STSS WRIT IHSS-1140 IHSS-1235 IHSS-1972 COGS-2120 COGS-4210 COGS-4410 COGS-4420 COGS-4640 COGS-4880 COGS-4960 PHIL-4960 PHIL-4961 COGS-2120 http://catalog.rpi.edu/ 4 This course is an introduction to the new and quickly growing field of cognitive science. Cognitive Science is a highly interdisciplinary field of study of mind at the intersection of philosophy, psychology, computer science, neuroscience, linguistics, and anthropology. This is a communication-intensive course. Introduction to Cognitive Science 2120 COGS-2120 COGS-2340 http://catalog.rpi.edu/ 4 This course offers a survey of scientific and applied approaches to the study of human language, highlighting the endlessly surprising nature of this sophisticated mode of communication. A sampling of the topics to be covered: phonology, morphology, syntax, semantics, lexicography, psycholinguistics, and historical linguistics. Lecture course. Introduction to Linguistics 2340 COGS-2340 COGS-4210 http://catalog.rpi.edu/ 4 Recent advances in Cognitive Science, Computer Science and Mathematics, have resulted in the ability to develop computer programs that implement Probabilistic Cognitive Models (PCMs). The cognitive models that this course covers are based on approximate Bayesian Inference implemented by Markov Chain Monte Carlo and Variational techniques that have made this approach tractable. The objective of this course is to enable the student to develop models of cognition in a Bayesian framework. Cognitive Modeling 4210 COGS-4210 COGS-4330 http://catalog.rpi.edu/ 4 This survey course is intended as an introduction to Cognitive Neuroscience. The topics covered will focus on exploring the neural underpinnings for cognitive processes, such as sensation, language, attention, motor control, executive functions, social communication, emotions, consciousness, and learning/memory. Basic aspects of nervous system function and neuroanatomy, brain development/evolution, structural and functional imaging techniques, and other research methods used in Cognitive Neuroscience will be discussed. Introduction to Cognitive Neuroscience 4330 COGS-4330 COGS-4360 http://catalog.rpi.edu/ 4 This course is an introduction to the role of physiological mechanisms in behavioral processes. There will be detailed examination and discussion of the involvement of biological systems in feeding and drinking, sexual behavior, sleep and arousal, learning and memory, psychopathology and psychopharmacology. Behavioral Neuroscience 4360 COGS-4360 COGS-4550 http://catalog.rpi.edu/ 4 4550 COGS-4550 COGS-4610 http://catalog.rpi.edu/ 4 This seminar course is a detailed examination of the mind-brain relationship, through study of the stress response. Stress is simply defined as any challenge to an individual's homeostasis or balance. This course will explore the neurobiological underpinnings of the stress response, with particular focus on how stressors can alter perception, affective and cognitive processing in the individual, which can in turn feedback to alter the general health of the individual (body and mind/brain). Stress and the Brain 4610 COGS-4610 COGS-4960 http://catalog.rpi.edu/ 4 An advanced course concerned with selected topics in cognitive science. Topics in Cognitive Science 4960 COGS-4960 COGS-4990 http://catalog.rpi.edu/ 4 Students conduct original scholarly projects: original research, theoretical or analytical reviews of the literature, or computer simulations. Students prepare written reports relating to this project, under the supervision of a faculty member. This is a communication-intensive course. Undergraduate Thesis 4990 COGS-4990 COGS-6210 http://catalog.rpi.edu/ 4 This is a graduate level course that introduces the student to computational cognitive modeling. Cognitive modeling is the simulation of human cognitive, perceptual, and motor processes based on a cognitive architecture. The benefit of cognitive modeling is that it facilitates the testing of ideas about human processes through comparison of model data with empirical data. This course covers ACT-R, a symbolic architecture and LEABRA, a neural-level architecture. Cognitive Modeling I 6210 COGS-6210 COGS-6960 http://catalog.rpi.edu/ 4 An advanced course concerned with selected topics in cognitive science. Topics in Cognitive Science 6960 COGS-6960 COGS-6961 http://catalog.rpi.edu/ 2 6961 COGS-6961 COGS-6962 http://catalog.rpi.edu/ 4 6962 COGS-6962 COGS-6963 http://catalog.rpi.edu/ 4 6963 COGS-6963 COGS-6980 http://catalog.rpi.edu/ 1 Active participation in a master's-level project, under the supervision of a faculty adviser, leading to a master's project report. Grades S or U are assigned at the end of the semester. If recommended by the adviser, the master's project may be accepted by the Office of Graduate Education to be archived in the library. Master's Project 6980 COGS-6980 COGS-6990 http://catalog.rpi.edu/ 1 Active participation in research, under the supervision of a faculty adviser, leading to a master's thesis. Grades of S or U are assigned by the adviser each term to reflect the student's research progress for the given semester. Once the thesis has been presentend, approved by the adviser, and accepted by the Office of Graduate Education, it will be archived in a standard format in the library. Master's Thesis 6990 COGS-6990 COGS-9990 http://catalog.rpi.edu/ 1 Active participation in research, under the supervision of a faculty adviser, leading to a doctoral dissertation. Grades of S or U are assigned by the adviser each term to reflect the student's research progress for the given semester. Once the dissertation has been publicly defended, approved by the doctoral committee, and accepted by the Office of Graduate Education, it will be archived in a standard format in the library. Doctoral Thesis 9990 COGS-9990 CSCI-1100 http://catalog.rpi.edu/ 4 An introduction to computer programming algorithm design and analysis. Additional topics include basic computer organization; internal representation of scalar and array data; use of top-down design and subprograms to tackle complex problems; abstract data types. Enrichment material as time allows. Interdisciplinary case studies, numerical and nonnumerical applications. Students who have passed CSCI 1200 cannot register for this course. Computer Science I 1100 CSCI-1100 CSCI-1190 http://catalog.rpi.edu/ 1 This course teaches elementary programming concepts using the MATLAB environment for engineering students with little or no prior programming experience. Concepts include variables, looping, and function calls. Students cannot get credit for CSCI 1190 after earning credit for CSCI 1100 or any higher-level CSCI course. Beginning Programming for Engineers 1190 CSCI-1190 CSCI-1200 http://catalog.rpi.edu/ 4 Programming concepts: functions, parameter passing, pointers, arrays, strings, structs, classes, templates. Mathematical tools: sets, functions, and relations, order notation, complexity of algorithms, proof by induction. Data structures and their representations: data abstraction and internal representation, sequences, trees, binary search trees, associative structures. Algorithms: searching and sorting, generic algorithms, iterative and recursive algorithms. Methods of testing correctness and measuring performance. Data Structures 1200 CSCI-1200 CSCI-2200 http://catalog.rpi.edu/ 4 This course introduces important mathematical and theoretical tools for computer science, including topics from set theory, combinatorics, and probability theory, and then proceeds to automata theory, the Turing Machine model of computation, and notions of computational complexity. The course will emphasize formal reasoning and proof techniques. Foundations of Computer Science 2200 CSCI-2200 CSCI-2300 http://catalog.rpi.edu/ 4 Data structures and algorithms, and the mathematical techniques necessary to design and analyze them. Basic data structures: lists, associative structures, trees. Mathematical techniques for designing algorithms and analyzing worst-case and expected-case algorithm efficiency. Advanced data structures: balanced trees, tries, heaps, priority queues, graphs. Searching, sorting. Algorithm design techniques: dynamic programming, greedy algorithms, divide-and-conquer, backtracking. Example graph, string, geometric, and numeric algorithms. Introduction to Algorithms 2300 CSCI-2300 CSCI-2500 http://catalog.rpi.edu/ 4 Introduction to computer organization, assembler language, and operating systems. Computer systems organization: processors, memory, I/O. Digital logic: gates, Boolean algebra, digital logic circuits, memory, buses. Microprogramming. Machine level: instruction formats, addressing modes, instruction types, flow of control. Operating systems: virtual memory, virtual I/O instructions, processes, interprocess communication. Numeric representation. Assembler language: the assembly process, macros, linking, loading. Advanced architectures: RISC architectures, parallel architectures. Computer Organization 2500 CSCI-2500 CSCI-2600 http://catalog.rpi.edu/ 4 A study of important concepts in software design, implementation, and testing. Topics include specification, abstraction with classes, design principles and patterns, testing, refactoring, the software development process, GUI and event-driven programming, and cloud-based programming. The course also introduces implementation and testing tools, including IDEs, revision control systems, and other frameworks. The overarching goal of the course is for students to learn how to write correct and maintainable software. Principles of Software 2600 CSCI-2600 CSCI-2960 http://catalog.rpi.edu/ 1 2960 CSCI-2960 CSCI-4020 http://catalog.rpi.edu/ 4 This course presents fundamental ideas and techniques of modern algorithm design and analysis. After completing this course, students should be able to formally analyze and design efficient algorithms for a variety of computational problems. Topics covered include Greedy Algorithms, Dynamic Programming, Network Flow, NP-Completeness, Linear Programming, Network Algorithms, as well as probabilistic and approximate algorithms. Design and Analysis of Algorithms 4020 CSCI-4020 CSCI-4150 http://catalog.rpi.edu/ 4 Topics and techniques of artificial intelligence using the language LISP. Topics include search, knowledge representation, expert systems, theorem proving, natural language interfaces, learning, game playing, and computer vision. Techniques include pattern matching, data-driven programming, substitution rules, frames, heuristic search, transition networks, neural networks, and evolutionary computation. Development of programming proficiency in LISP is emphasized. Introduction to Artificial Intelligence 4150 CSCI-4150 CSCI-4210 http://catalog.rpi.edu/ 4 Discussion of various aspects of computer operating systems design and implementation. Topics include I/O programming, concurrent processes and synchronization problems, process management and scheduling of processes, virtual memory management, device management, file systems, deadlock problems, system calls, and interprocess communication. Programming projects are required. Operating Systems 4210 CSCI-4210 CSCI-4260 http://catalog.rpi.edu/ 4 Fundamental concepts and methods of graph theory and its applications to computing and the social and natural sciences. Topics include graphs as models, representation of graphs, trees, distances, matchings, connectivity, flows in networks, graph colorings, Hamiltonian cycles, traveling salesman problem, planarity. All concepts, methods, and applications are presented through a sequence of exercises and problems, many of which are done with the help of novel software systems for combinatorial computing. Graph Theory 4260 CSCI-4260 CSCI-4320 http://catalog.rpi.edu/ 4 Techniques and methods for parallel programming: models of parallel machines and programs, efficiency and complexity of parallel algorithms. Paradigms of parallel programming and corresponding extensions to sequential programming languages. Overview of parallel languages and coordination languages and models; programming on networks of workstations. Basic parallel algorithms: elementary computation, matrix multiplication, sorting; sample scientific application. Parallel Programming 4320 CSCI-4320 CSCI-4380 http://catalog.rpi.edu/ 4 Discussion of the state of practice in modern database systems, with an emphasis on relational systems. Topics include database design, database system architecture, SQL, normalization techniques, storage structures, query processing, concurrency control, recovery, security, and new directions such as object-oriented and distributed database systems. Students gain hands-on experience with commercial database systems and interface building tools. Programming projects are required. Database Systems 4380 CSCI-4380 CSCI-4400 http://catalog.rpi.edu/ 3 Informatics covers a broad range of disciplines addressing challenges in the explosion of data and information resources. Xinformatics provides commonality for implementations in specific disciplines, e.g. X=astro, geo. Informatics' theoretical bases are information and computer science, cognitive science, social science, library science, aggregating these studies and adding the practice of information processing, and the engineering of information systems. This course grounds the material that students will learn in discipline areas by coursework and project assignments. X-informatics 4400 CSCI-4400 CSCI-4440 http://catalog.rpi.edu/ 4 Software system design methodology emphasizing use of object oriented modeling of application domains and of software systems and emphasizing the roles of written and oral communication in software engineering. Project management and software testing. Individual and team projects include specification, software architecture, user interfaces, and documentation of the phases of a project. This is a communication-intensive course. Software Design and Documentation 4440 CSCI-4440 CSCI-4450 http://catalog.rpi.edu/ 4 The goal of this course is to introduce students to program analysis and its many applications in software engineering, particularly in improving software quality and software productivity. Concretely, students who successfully complete this course should be able to: (1) understand and apply program analysis techniques, such as dataflow analysis and type-based analysis; (2) implement program analysis; (3) understand and apply software testing techniques, such as black-box testing and white-box testing; and (4) understand and apply refactoring techniques. Principles of Program Analysis 4450 CSCI-4450 CSCI-4540 http://catalog.rpi.edu/ 4 This course follows Game Development I. Students work in interdisciplinary teams to create one large-scale 3D game, working from concept to public release. Projects may include games, educational games, serious games and simulations, and interactive artworks, and will focus on creative design, technical execution, and use of game design principles. The course builds on skills and knowledge in previous courses in the GSAS core, including game design, game mechanics, game programming, art, and narrative. Game Development II 4540 CSCI-4540 CSCI-4550 http://catalog.rpi.edu/ 4 Visualizing data is a key step in understanding many problems. This course is designed to introduce students to methods of visualizing many different types of data, such as images, three-dimensional surfaces, flow fields, and medical data. Both existing visualization software and program custom visualizations using C++ and OpenGL will be used. Course activities include discussion of recent and classic research papers, weekly homework assignments, in-class critiques of visualization artifacts, and a final project to explore creative uses of these techniques. This is a communication-intensive course. Interactive Visualization 4550 CSCI-4550 CSCI-4800 http://catalog.rpi.edu/ 4 A survey of numerical methods for scientific and engineering problems. Topics include numerical solution of linear and nonlinear algebraic equations, interpolation and least squares approximations, numerical integration and differentiation, eigenvalue problems, and an introduction to the numerical solution of ordinary differential equations. Emphasis is placed on efficient computational procedures including the use of library and student written procedures using high-level software such as MATLAB. Numerical Computing 4800 CSCI-4800 CSCI-4820 http://catalog.rpi.edu/ 4 Derivation, analysis, and use of computational procedures for solving differential equations. Topics covered include ordinary differential equations (both initial value and boundary value problems) and partial differential equations. Runge-Kutta and multistep methods for initial value problems. Finite difference methods for partial differential equations including techniques for heat conduction, wave propagation, and potential problems. Basic convergence and stability theory. Introduction to Numerical Methods for Differential Equations 4820 CSCI-4820 CSCI-4961 http://catalog.rpi.edu/ 4 4961 CSCI-4961 CSCI-4962 http://catalog.rpi.edu/ 4 4962 CSCI-4962 CSCI-4964 http://catalog.rpi.edu/ 4 4964 CSCI-4964 CSCI-4967 http://catalog.rpi.edu/ 4 4967 CSCI-4967 CSCI-4969 http://catalog.rpi.edu/ 4 4969 CSCI-4969 CSCI-4976 http://catalog.rpi.edu/ 4 4976 CSCI-4976 CSCI-6360 http://catalog.rpi.edu/ 4 A survey of fundamental issues in design of efficient programs for parallel computers. The topics discussed include models of parallel machines and programs, efficiency of parallel algorithms, programming styles for shared memory, message passing, data parallelism, and using MPI in scientific parallel programs. Parallel programming project required. Parallel Computing 6360 CSCI-6360 CSCI-6400 http://catalog.rpi.edu/ 3 Informatics covers a broad range of disciplines addressing challenges in the explosion of data and information resources. Xinformatics provides commonality for implementations in specific disciplines, e.g. X=astro, geo. Informatics' theoretical bases are information and computer science, cognitive science, social science, library science, aggregating these studies and adding the practice of information processing, and the engineering of information systems. This course grounds the material that students will learn in discipline areas by coursework and project assignments. X-informatics 6400 CSCI-6400 CSCI-6450 http://catalog.rpi.edu/ 4 The goal of this course is to introduce students to program analysis and its many applications in software engineering, particularly in improving software quality and software productivity. Concretely, students who successfully complete this course should be able to: (1) understand and apply program analysis techniques, such as dataflow analysis and type-based analysis; (2) implement program analysis; (3) understand and apply software testing techniques, such as black-box testing and white-box testing; and (4) understand and apply refactoring techniques. Principles of Program Analysis 6450 CSCI-6450 CSCI-6550 http://catalog.rpi.edu/ 4 Visualizing data is a key step in understanding many problems. This course is designed to introduce students to methods of visualizing many different types of data, such as images, three-dimensional surfaces, flow fields, and medical data. Both existing visualization software and program custom visualizations using C++ and OpenGL will be used. Course activities include discussion of recent and classic research papers, weekly homework assignments, in-class critiques of visualization artifacts, and a final project to explore creative uses of these techniques. This is a communication-intensive course. Interactive Visualization 6550 CSCI-6550 CSCI-6840 http://catalog.rpi.edu/ 4 Numerical methods and analysis for linear and nonlinear PDEs with applications from heat conduction, wave propagation, solid and fluid mechanics, and other areas. Basic concepts of stability and convergence (Lax equivalence theorem, CFL condition, energy methods). Methods for parabolic problems (finite differences, method of lines, ADI, operator splitting), methods for hyperbolic problems (vector systems and characteristics, dissipation and dispersion, shocks capturing and tracking schemes), methods for elliptic problems (finite difference and finite volume methods). Numerical Solution of Partial Differential Equations 6840 CSCI-6840 CSCI-6900 http://catalog.rpi.edu/ 0 Presentation of current developments in computer science. Reports by students. Computer Science Seminar 6900 CSCI-6900 CSCI-6960 http://catalog.rpi.edu/ 0 Topics in Computer Science 6960 CSCI-6960 CSCI-6961 http://catalog.rpi.edu/ 3 6961 CSCI-6961 CSCI-6962 http://catalog.rpi.edu/ 4 6962 CSCI-6962 CSCI-6964 http://catalog.rpi.edu/ 4 6964 CSCI-6964 CSCI-6967 http://catalog.rpi.edu/ 4 6967 CSCI-6967 CSCI-6969 http://catalog.rpi.edu/ 4 6969 CSCI-6969 CSCI-6970 http://catalog.rpi.edu/ 1 Active participation in a semester-long project, under the supervision of a faculty adviser. A Professional Project often serves as a culminating experience for a Professional Master's program but, with departmental or school approval, can be used to fulfill other program requirements. With approval, students may register for more than one Professional Project. Professional Projects must result in documentation established by each department or school, but are not submitted to the Office of Graduate Education and are not archived in the library. Grades of A, B, C, or F are assigned by the faculty adviser at the end of the semester. If not completed on time, a formal Incomplete grade may be assigned by the faculty adviser, listing the work remaining to be completed and the time limit for completing this work. Professional Project 6970 CSCI-6970 CSCI-6980 http://catalog.rpi.edu/ 1 Active participation in a master's-level project, under the supervision of a faculty adviser, leading to a master's project report. Grades S or U are assigned at the end of the semester. If recommended by the adviser, the master's project may be accepted by the Office of Graduate Education to be archived in the library. Master's Project 6980 CSCI-6980 CSCI-6990 http://catalog.rpi.edu/ 1 Active participation in research, under the supervision of a faculty adviser, leading to a master's thesis. Grades of S or U are assigned by the adviser each term to reflect the student's research progress for the given semester. Once the thesis has been presentend, approved by the adviser, and accepted by the Office of Graduate Education, it will be archived in a standard format in the library. Master's Thesis 6990 CSCI-6990 CSCI-9990 http://catalog.rpi.edu/ 1 Active participation in research, under the supervision of a faculty adviser, leading to a doctoral dissertation. Grades of IP are assigned until the dissertation has been publicly defended, approved by the doctoral committee, and accepted by the Office of Graduate Education to be archived in a standard format in the library. Grades will then be listed as S. Dissertation 9990 CSCI-9990 COGS-4410 http://catalog.rpi.edu/ 4 Research in Cognitive Science and Artificial Intelligence (AI) is driven by data. Researchers in these fields collect, manipulate, model and analyze data generated by real-world processes. Since the amount of data available has grown exponentially, the ability to automate these tasks through computer programs is essential. Specifically, probabilistic and statistical computing are needed to learn from the data. The objectives of this course are for the student to perform exploratory data analysis and to acquire the basics of statistical and machine learning in order to model real-world datasets. Programming for Cognitive Science and Artificial Intelligence 4410 COGS-4410 COGS-4420 http://catalog.rpi.edu/ 4 This course introduces students to basic concepts and methods of artificial intelligence and their applications in computer games. The topics include decision making, movement, path finding, and AI for human-like characters. This course will take the form of a combination of lectures, presentations by students, class discussions, and independent study. Game AI 4420 COGS-4420 COGS-4430 http://catalog.rpi.edu/ 4 Digital gaming is one of the most rapidly developing fields. The effort required for developing games is not trivial. To make a game fun to play, the design of the game levels and/or the AI-driven opponents need to be intelligent and adaptive to the players' strategies and skills. In this course, students will learn and explore using machine learning techniques to automate the design process of digital games. The course will cover basic and advanced topics in Artificial Intelligence and Learning, such as Decision Trees, Neural Networks, Genetic Algorithms, and Reinforcement Learning. Students will gain hands-on experience in applying these techniques in computer games. The course will also introduce psychological theories and studies about people's decision-making and emotional processes and how they are related to the players' experience in games. This course will take the form of a combination of lectures, presentations by students, class discussions, and independent study. Learning and Advanced Game AI 4430 COGS-4430 COGS-4440 http://catalog.rpi.edu/ 4 "Sensibilities"--a special ART_X@Rensselaer (Art Across the Curriculum) seminar--draws from the tremendous resource of EMPAC to inspire students to cultivate writing skills through the cross-disciplinary theme of the senses/perception. During the semester students will have opportunities to observe unique art/science presentations and performances in an intimate setting at EMPAC, providing rich experiences for discussions and writing. Classes include reading science and art texts, as well as writing workshops to develop authorial voice and experimentation. Sensibilities 4440 COGS-4440 COGS-4560 http://catalog.rpi.edu/ 4 This course will explore the different strategies used by different languages to fulfill the same needs of human communication. A sampling of topics: quickly learning the basics of a new language using linguistic principles; cross-linguistic knowledge elicitation and engineering; principles of generative grammar; space, time, agency, and other linguistic phenomena viewed cross-linguistically. Cross-linguistic Perspectives 4560 COGS-4560 COGS-4961 http://catalog.rpi.edu/ 4 4961 COGS-4961 COGS-4962 http://catalog.rpi.edu/ 4 4962 COGS-4962 COGS-6410 http://catalog.rpi.edu/ 4 This course is a graduate course that teaches Cognitive Science and Artificial Intelligence concepts by enabling the student to develop and understand computer programs that implement them. It covers data collection and analysis, task environments, natural language, cognitive architectures, and learning. Some previous programming experience is very beneficial but not required. Programming for Cognitive Science and Artificial Intelligence 6410 COGS-6410 COGS-6430 http://catalog.rpi.edu/ 4 Digital gaming is one of the most rapidly developing fields. The effort required for developing games is not trivial. To make a game fun to play, the design of the game levels and/or the AI-driven opponents need to be intelligent and adaptive to the players' strategies and skills. In this course, students will learn and explore using machine learning techniques to automate the design process of digital games. The course will cover basic and advanced topics in Artificial Intelligence and Learning, such as Decision Trees, Neural Networks, Genetic Algorithms, and Reinforcement Learning. Students will gain hands-on experience in applying these techniques in computer games. The course will also introduce psychological theories and studies about people's decision-making and emotional processes and how they are related to the players' experience in games. This course will take the form of a combination of lectures, presentations by students, class discussions, and independent study. Learning and Advanced Game AI 6430 COGS-6430 COGS-6560 http://catalog.rpi.edu/ 3 This course will explore the different strategies used by different languages to fulfill the same needs of human communication. A sampling of topics: quickly learning the basics of a new language using linguistic principles; cross-linguistic knowledge elicitation and engineering; principles of generative grammar; space, time, agency, and other linguistic phenomena viewed cross-linguistically. Cross-linguistic Perspectives 6560 COGS-6560 COGS-6964 http://catalog.rpi.edu/ 4 6964 COGS-6964 COGS-6968 http://catalog.rpi.edu/ 2 6968 COGS-6968 CSCI-2961 http://catalog.rpi.edu/ 1 2961 CSCI-2961 CSCI-4100 http://catalog.rpi.edu/ 4 Introduction to the theory, algorithms, and applications of machine learning (supervised, reinforcement, and unsupervised) from data: What is learning? Is learning feasible? How can we do it? How can we do it well? The course offers a mix of theory, technique, and application with additional selected topics chosen from Pattern Recognition, Decision Trees, Neural Networks, RBF's, Bayesian Learning, PAC Learning, Support Vector Machines, Gaussian processes, and Hidden Markov Models. Machine Learning from Data 4100 CSCI-4100 CSCI-4220 http://catalog.rpi.edu/ 4 Programming with an overview of the principles of computer networks, including a detailed look at the OSI reference model and various popular network protocol suites. Concentration on Unix interprocess communication (IPC), network programming using TCP and UDP, as well as client-side and mobile programming. Programming projects are required. Network Programming 4220 CSCI-4220 CSCI-4250 http://catalog.rpi.edu/ 4 This course will offer an introduction to network science and a review of current research in this area. Classes will interchangeably present chapters from the textbook and related current research. The emphasis will be on the mathematical background of network science: graphs and networks; random networks and various types of scale-free networks; network properties such as assortativity, mobility, robustness, social networks, and communities; and dynamics of spreading in networks. Frontiers of Network Science 4250 CSCI-4250 CSCI-4310 http://catalog.rpi.edu/ 4 This course introduces Linux kernel programming basics and starts by examining how Berkeley sockets bridge the user-kernel gap. The remainder of the course is spent looking into transport layer (e.g., TCP) and network layer (e.g., IP) implementations. Students do both individual and group programming projects. In addition to coding, there are detailed write-ups and peer reviews in this course. This is a communication-intensive course. Networking in the Linux Kernel 4310 CSCI-4310 CSCI-4340 http://catalog.rpi.edu/ 4 This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems. Ontologies encode term meanings and are used to improve communication and enable computer programs to function more effectively. Class participants learn how to use ontologies in Web-based applications and evaluate ontologies for reuse. Participants read relevant papers, learn how to critically review ontology papers and ontologies, and participate in group project(s) designing, using, and evaluating ontologies. Ontologies 4340 CSCI-4340 CSCI-4350 http://catalog.rpi.edu/ 3 Data science is advancing the inductive conduct of science and is driven by the greater volumes, complexity, and heterogeneity of data being made available over the Internet. It combines aspects of data management, library science, computer science, and physical science. It is changing the way all of these disciplines do both their individual and collaborative work. Key methodologies in application areas based on real research experience are taught. Data Science 4350 CSCI-4350 CSCI-4370 http://catalog.rpi.edu/ 4 Data and Society provides a broad overview of how society is leveraging and responding to the social, organizational, policy, and technical opportunities and challenges of a data-driven world. Course themes focus on various aspects of the data ecosystem, data and innovation, and data and the broader community. Assignments build writing, presentation, and critical thinking, and assessment skills, all of which are important for professional success. This is a communication-intensive course. Data and Society 4370 CSCI-4370 CSCI-4390 http://catalog.rpi.edu/ 4 This course will provide an introductory survey of the main topics in data mining and knowledge discovery in databases (KDD), including: classification, clustering, association rules, sequence mining, similarity search, deviation detection, and so on. Emphasis will be on the algorithmic and system issues in KDD, as well as on applications such as Web mining, multimedia mining, bioinformatics, geographical information systems, etc. Data Mining 4390 CSCI-4390 CSCI-4430 http://catalog.rpi.edu/ 4 This course is a study of the important concepts found in current programming languages. Topics include language processing (lexical analysis, parsing, type-checking, interpretation and compilation, run-time environment), the role of abstraction (data abstraction and control abstraction), programming paradigms (procedural, functional, object-oriented, logic-oriented, generic), and formal language definition. Programming Languages 4430 CSCI-4430 CSCI-4460 http://catalog.rpi.edu/ 4 This course focuses on software development techniques in support of large-scale software projects and maintenance. Specific topics include various programming paradigms and techniques, approaches to testing and automation, debugging, refactoring, and inheriting code. Individual and team assignments are required, including programming assignments. Project topics include text processing, building a search engine, and the like. This is a communication-intensive course. Large-Scale Programming and Testing 4460 CSCI-4460 CSCI-4480 http://catalog.rpi.edu/ 3 A survey of the fundamental issues necessary for the design, analysis, control, and implementation of robotic systems. The mathematical description of robot manipulators in terms of kinematics and dynamics. Hardware components of a typical robot arm. Path following, control, and sensing. Examples of several currently available manipulators. Robotics I 4480 CSCI-4480 CSCI-4510 http://catalog.rpi.edu/ 4 This course explores the principles of distributed systems, emphasizing fundamental issues underlying the design of such systems: communication, coordination, synchronization, and fault-tolerance. Key algorithms and theoretical results will be studied and students will explore how these foundations play out in modern systems and applications. Distributed Systems and Algorithms 4510 CSCI-4510 CSCI-4960 http://catalog.rpi.edu/ 3 Topics in Computer Science 4960 CSCI-4960 CSCI-4966 http://catalog.rpi.edu/ 4 4966 CSCI-4966 CSCI-4968 http://catalog.rpi.edu/ 4 4968 CSCI-4968 CSCI-4970 http://catalog.rpi.edu/ 4 4970 CSCI-4970 CSCI-6100 http://catalog.rpi.edu/ 4 Introduction to the theory, algorithms, and applications of machine learning (supervised, reinforcement, and unsupervised) from data: What is learning? Is learning feasible? How can it be done? How can it be done well? The course offers a mix of theory, technique, and application with additional selected topics chosen from Pattern Recognition, Decision Trees, Neural Networks, RBF's, Bayesian Learning, PAC Learning, Support Vector Machines, Gaussian processes, and Hidden Markov Models. Machine Learning from Data 6100 CSCI-6100 CSCI-6250 http://catalog.rpi.edu/ 4 This course will offer an introduction to network science and a review of current research in this area. Classes will interchangeably present chapters from the textbook and related current research. The emphasis will be on the mathematical background of network science: graphs and networks; random networks and various types of scale-free networks; network properties such as assortativity, mobility, robustness, social networks, and communities; and dynamics of spreading in networks. Frontiers of Network Science 6250 CSCI-6250 CSCI-6310 http://catalog.rpi.edu/ 4 This course introduces Linux kernel programming basics and starts by examining how Berkeley sockets bridge the user-kernel gap. The remainder of the course is spent looking into transport layer (e.g., TCP) and network layer (e.g., IP) implementations. Students do both individual and group programming projects. In addition to coding, there are detailed write-ups and peer reviews in this course. Networking in the Linux Kernel 6310 CSCI-6310 CSCI-6340 http://catalog.rpi.edu/ 4 This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems. Ontologies encode term meanings and are used to improve communication and enable computer programs to function more effectively. Class participants learn how to use ontologies in web-based applications and evaluate ontologies for reuse. Participants read relevant papers, learn how to critically review ontology papers and ontologies, and participate in group project(s) designing, using, and evaluating ontologies. Ontologies 6340 CSCI-6340 CSCI-6350 http://catalog.rpi.edu/ 3 Data science is advancing the inductive conduct of science and is driven by the greater volumes, complexity, and heterogeneity of data being made available over the Internet. It combines aspects of data management, library science, computer science, and physical science. It is changing the way all of these disciplines do both their individual and collaborative work. Key methodologies in application areas based on real research experience are taught. Data Science 6350 CSCI-6350 CSCI-6390 http://catalog.rpi.edu/ 4 This course will provide an introductory survey of the main topics in data mining and knowledge discovery in databases (KDD), including: classification, clustering, association rules, sequence mining, similarity search, deviation detection, and so on. Emphasis will be on the algorithmic and system issues in KDD, as well as on applications such as Web mining, multimedia mining, bioinformatics, geographical information systems, etc. Data Mining 6390 CSCI-6390 CSCI-6460 http://catalog.rpi.edu/ 4 This course focuses on software development techniques in support of large-scale software projects and maintenance. Specific topics include various programming paradigms and techniques, approaches to testing and automation, debugging, refactoring, and inheriting code. Individual and team assignments are required, including programming assignments. Project topics include text processing, building a search engine, and the like. Large-Scale Programming and Testing 6460 CSCI-6460 CSCI-6510 http://catalog.rpi.edu/ 4 This course explores the principles of distributed systems, emphasizing fundamental issues underlying the design of such systems: communication, coordination, synchronization, and fault-tolerance. Key algorithms and theoretical results will be studied and students will explore how these foundations play out in modern systems and applications. Distributed Systems and Algorithms 6510 CSCI-6510 CSCI-6800 http://catalog.rpi.edu/ 4 Gaussian elimination, special linear systems (such as positive definite, banded, or sparse), introduction to parallel computing, iterative methods for linear systems (such as conjugate gradient and preconditioning), QR factorization and least squares problems, and eigenvalue problems. Computational Linear Algebra 6800 CSCI-6800 CSCI-6860 http://catalog.rpi.edu/ 4 Galerkin's method and extremal principles, finite element approximations (Lagrange, hierarchical and 3-D approximations, interpolation errors), mesh generation and assembly, adaptivity (h-, p-, hp-refinement). Error analysis and convergence rates. Perturbations resulting from boundary approximation, numerical integration, etc. Time dependent problems including parabolic and hyperbolic PDEs. Applications will be selected from several areas including heat conduction, wave propagation, potential theory, and solid and fluid mechanics. Finite Element Analysis 6860 CSCI-6860 COGS-4600 http://catalog.rpi.edu/ 4 Perception and thought are considered in terms of processes represented in the brain. The localization and lateralization of function are examined, drawing upon research on the behavioral effects of brain damage as well as brain-imaging studies and other approaches. Examples of topics include object recognition, memory, language, emotion, spatial ability, and motor processes. Cognition and the Brain 4600 COGS-4600 CSCI-4230 http://catalog.rpi.edu/ 4 A self-contained course that includes topics from number theory, basic cryptography, and protocol security. This is a hybrid course with sufficient depth in both theory and hands-on experience with network protocols. Topics include: Classical Cryptography, Block Ciphers (DES, AES), Information Theoretical Cryptography, Randomness, RNG and Stream Ciphers, Hash and MAC Algorithms, Public-Key Cryptography, Elliptic Curve Cryptography, Digital Signatures and Identification, Internet Attacks, Web Security, SSL and PGP. This is a communication-intensive course. Cryptography and Network Security I 4230 CSCI-4230 CSCI-4963 http://catalog.rpi.edu/ 4 4963 CSCI-4963 CSCI-4965 http://catalog.rpi.edu/ 4 4965 CSCI-4965 Introduction to Cognitive Science SPRING 2020 Section 01 Introduction to Cognitive Science SPRING 2020 Section 02 Introduction to Cognitive Science SPRING 2020 Section 03 Introduction to Linguistics SPRING 2020 Section 01 Introduction to Linguistics SPRING 2020 Section 02 Introduction to Linguistics SPRING 2020 Section 03 Introduction to Linguistics SPRING 2020 Section 04 Cognitive Modeling SPRING 2020 Section 01 Introduction to Cognitive Neuroscience SPRING 2020 Section 01 Introduction to Cognitive Neuroscience SPRING 2020 Section 02 Behavioral Neuroscience SPRING 2020 Section 01 Behavioral Neuroscience SPRING 2020 Section 02 SPRING 2020 Section 01 Stress and the Brain SPRING 2020 Section 01 Topics in Cognitive Science SPRING 2020 Section 01 Undergraduate Thesis SPRING 2020 Section 01 Undergraduate Thesis SPRING 2020 Section 02 Undergraduate Thesis SPRING 2020 Section 05 Undergraduate Thesis SPRING 2020 Section 06 Undergraduate Thesis SPRING 2020 Section 07 Undergraduate Thesis SPRING 2020 Section 08 Undergraduate Thesis SPRING 2020 Section 09 Undergraduate Thesis SPRING 2020 Section 11 Undergraduate Thesis SPRING 2020 Section 12 Undergraduate Thesis SPRING 2020 Section 13 Undergraduate Thesis SPRING 2020 Section 14 Cognitive Modeling I SPRING 2020 Section 01 Master's Project SPRING 2020 Section 01 Master's Project SPRING 2020 Section 02 Master's Project SPRING 2020 Section 03 Master's Project SPRING 2020 Section 07 Master's Thesis SPRING 2020 Section 02 Master's Thesis SPRING 2020 Section 03 Master's Thesis SPRING 2020 Section 04 Master's Thesis SPRING 2020 Section 06 Doctoral Thesis SPRING 2020 Section 01 Doctoral Thesis SPRING 2020 Section 02 Doctoral Thesis SPRING 2020 Section 03 Doctoral Thesis SPRING 2020 Section 04 Doctoral Thesis SPRING 2020 Section 05 Doctoral Thesis SPRING 2020 Section 06 Doctoral Thesis SPRING 2020 Section 07 Doctoral Thesis SPRING 2020 Section 08 Doctoral Thesis SPRING 2020 Section 09 Doctoral Thesis SPRING 2020 Section 11 Doctoral Thesis SPRING 2020 Section 13 Doctoral Thesis SPRING 2020 Section 14 Computer Science I SPRING 2020 Section 01 Computer Science I SPRING 2020 Section 02 Computer Science I SPRING 2020 Section 03 Computer Science I SPRING 2020 Section 04 Computer Science I SPRING 2020 Section 05 Computer Science I SPRING 2020 Section 06 Computer Science I SPRING 2020 Section 07 Computer Science I SPRING 2020 Section 11 Beginning Programming for Engineers SPRING 2020 Section 01 Beginning Programming for Engineers SPRING 2020 Section 02 Beginning Programming for Engineers SPRING 2020 Section 03 Beginning Programming for Engineers SPRING 2020 Section 04 Data Structures SPRING 2020 Section 01 Data Structures SPRING 2020 Section 02 Data Structures SPRING 2020 Section 03 Data Structures SPRING 2020 Section 04 Data Structures SPRING 2020 Section 05 Data Structures SPRING 2020 Section 06 Data Structures SPRING 2020 Section 07 Data Structures SPRING 2020 Section 08 Data Structures SPRING 2020 Section 09 Data Structures SPRING 2020 Section 10 Data Structures SPRING 2020 Section 11 Data Structures SPRING 2020 Section 12 Data Structures SPRING 2020 Section 13 Data Structures SPRING 2020 Section 14 Data Structures SPRING 2020 Section 15 Data Structures SPRING 2020 Section 16 Foundations of Computer Science SPRING 2020 Section 01 Foundations of Computer Science SPRING 2020 Section 02 Foundations of Computer Science SPRING 2020 Section 03 Foundations of Computer Science SPRING 2020 Section 04 Foundations of Computer Science SPRING 2020 Section 05 Introduction to Algorithms SPRING 2020 Section 01 Introduction to Algorithms SPRING 2020 Section 02 Introduction to Algorithms SPRING 2020 Section 03 Introduction to Algorithms SPRING 2020 Section 04 Introduction to Algorithms SPRING 2020 Section 05 Introduction to Algorithms SPRING 2020 Section 06 Introduction to Algorithms SPRING 2020 Section 07 Introduction to Algorithms SPRING 2020 Section 08 Computer Organization SPRING 2020 Section 01 Computer Organization SPRING 2020 Section 02 Computer Organization SPRING 2020 Section 03 Computer Organization SPRING 2020 Section 04 Computer Organization SPRING 2020 Section 05 Computer Organization SPRING 2020 Section 06 Principles of Software SPRING 2020 Section 01 SPRING 2020 Section 02 SPRING 2020 Section 03 SPRING 2020 Section 04 SPRING 2020 Section 05 SPRING 2020 Section 06 SPRING 2020 Section 07 SPRING 2020 Section 08 SPRING 2020 Section 09 SPRING 2020 Section 10 SPRING 2020 Section 11 Design and Analysis of Algorithms SPRING 2020 Section 01 Introduction to Artificial Intelligence SPRING 2020 Section 01 Operating Systems SPRING 2020 Section 01 Graph Theory SPRING 2020 Section 01 Parallel Programming SPRING 2020 Section 01 Database Systems SPRING 2020 Section 01 X-informatics SPRING 2020 Section 01 Software Design and Documentation SPRING 2020 Section 01 Software Design and Documentation SPRING 2020 Section 02 Software Design and Documentation SPRING 2020 Section 03 Principles of Program Analysis SPRING 2020 Section 01 Game Development II SPRING 2020 Section 01 Game Development II SPRING 2020 Section 02 Interactive Visualization SPRING 2020 Section 01 Numerical Computing SPRING 2020 Section 01 Numerical Computing SPRING 2020 Section 02 Introduction to Numerical Methods for Differential Equations SPRING 2020 Section 01 Parallel Computing SPRING 2020 Section 01 Numerical Solution of Partial Differential Equations SPRING 2020 Section 01 Computer Science Seminar SPRING 2020 Section 01 Topics in Computer Science SPRING 2020 Section 01 Professional Project SPRING 2020 Section 01 Master's Project SPRING 2020 Section 04 Master's Project SPRING 2020 Section 05 Master's Project SPRING 2020 Section 06 Master's Project SPRING 2020 Section 08 Master's Project SPRING 2020 Section 09 Master's Project SPRING 2020 Section 10 Master's Project SPRING 2020 Section 11 Master's Project SPRING 2020 Section 12 Master's Project SPRING 2020 Section 13 Master's Project SPRING 2020 Section 14 Master's Project SPRING 2020 Section 15 Master's Project SPRING 2020 Section 16 Master's Project SPRING 2020 Section 17 Master's Project SPRING 2020 Section 18 Master's Project SPRING 2020 Section 19 Master's Project SPRING 2020 Section 20 Master's Project SPRING 2020 Section 21 Master's Project SPRING 2020 Section 22 Master's Project SPRING 2020 Section 23 Master's Project SPRING 2020 Section 24 Master's Project SPRING 2020 Section 25 Master's Project SPRING 2020 Section 26 Master's Project SPRING 2020 Section 27 Master's Project SPRING 2020 Section 50 Master's Thesis SPRING 2020 Section 01 Master's Thesis SPRING 2020 Section 05 Master's Thesis SPRING 2020 Section 07 Master's Thesis SPRING 2020 Section 08 Master's Thesis SPRING 2020 Section 09 Master's Thesis SPRING 2020 Section 10 Master's Thesis SPRING 2020 Section 11 Master's Thesis SPRING 2020 Section 12 Master's Thesis SPRING 2020 Section 13 Master's Thesis SPRING 2020 Section 14 Master's Thesis SPRING 2020 Section 15 Master's Thesis SPRING 2020 Section 16 Master's Thesis SPRING 2020 Section 17 Master's Thesis SPRING 2020 Section 18 Master's Thesis SPRING 2020 Section 19 Master's Thesis SPRING 2020 Section 20 Master's Thesis SPRING 2020 Section 21 Master's Thesis SPRING 2020 Section 22 Master's Thesis SPRING 2020 Section 23 Master's Thesis SPRING 2020 Section 24 Master's Thesis SPRING 2020 Section 26 Master's Thesis SPRING 2020 Section 27 Master's Thesis SPRING 2020 Section 28 Master's Thesis SPRING 2020 Section 29 Master's Thesis SPRING 2020 Section 30 Master's Thesis SPRING 2020 Section 31 Master's Thesis SPRING 2020 Section 35 Master's Thesis SPRING 2020 Section 36 Dissertation SPRING 2020 Section 01 Dissertation SPRING 2020 Section 02 Dissertation SPRING 2020 Section 03 Dissertation SPRING 2020 Section 04 Dissertation SPRING 2020 Section 05 Dissertation SPRING 2020 Section 06 Dissertation SPRING 2020 Section 07 Dissertation SPRING 2020 Section 08 Dissertation SPRING 2020 Section 09 Dissertation SPRING 2020 Section 10 Dissertation SPRING 2020 Section 11 Dissertation SPRING 2020 Section 12 Dissertation SPRING 2020 Section 13 Dissertation SPRING 2020 Section 14 Dissertation SPRING 2020 Section 16 Dissertation SPRING 2020 Section 17 Dissertation SPRING 2020 Section 18 Dissertation SPRING 2020 Section 19 Dissertation SPRING 2020 Section 20 Dissertation SPRING 2020 Section 21 Dissertation SPRING 2020 Section 22 Dissertation SPRING 2020 Section 23 Dissertation SPRING 2020 Section 24 Dissertation SPRING 2020 Section 25 Dissertation SPRING 2020 Section 26 Dissertation SPRING 2020 Section 27 Dissertation SPRING 2020 Section 28 Dissertation SPRING 2020 Section 29 Dissertation SPRING 2020 Section 32 Dissertation SPRING 2020 Section 34 Introduction to Cognitive Science FALL 2020 Section 01 Introduction to Cognitive Science FALL 2020 Section 02 Introduction to Cognitive Science FALL 2020 Section 03 Introduction to Linguistics FALL 2020 Section 04 Introduction to Linguistics FALL 2020 Section 05 Introduction to Cognitive Neuroscience FALL 2020 Section 01 Introduction to Cognitive Neuroscience FALL 2020 Section 02 Programming for Cognitive Science and Artificial Intelligence FALL 2020 Section 01 Game AI FALL 2020 Section 02 Learning and Advanced Game AI FALL 2020 Section 01 Sensibilities FALL 2020 Section 01 Cross-linguistic Perspectives FALL 2020 Section 01 Topics in Cognitive Science FALL 2020 Section 01 FALL 2020 Section 01 Undergraduate Thesis FALL 2020 Section 02 Undergraduate Thesis FALL 2020 Section 06 Undergraduate Thesis FALL 2020 Section 08 Master's Project FALL 2020 Section 01 Master's Project FALL 2020 Section 02 Master's Thesis FALL 2020 Section 01 Master's Thesis FALL 2020 Section 02 Master's Thesis FALL 2020 Section 03 Master's Thesis FALL 2020 Section 04 Master's Thesis FALL 2020 Section 05 Master's Thesis FALL 2020 Section 06 Master's Thesis FALL 2020 Section 07 Doctoral Thesis FALL 2020 Section 01 Doctoral Thesis FALL 2020 Section 02 Doctoral Thesis FALL 2020 Section 03 Doctoral Thesis FALL 2020 Section 04 Doctoral Thesis FALL 2020 Section 05 Doctoral Thesis FALL 2020 Section 06 Doctoral Thesis FALL 2020 Section 07 Doctoral Thesis FALL 2020 Section 08 Doctoral Thesis FALL 2020 Section 09 Doctoral Thesis FALL 2020 Section 10 Doctoral Thesis FALL 2020 Section 11 Doctoral Thesis FALL 2020 Section 12 Computer Science I FALL 2020 Section 01 Computer Science I FALL 2020 Section 02 Computer Science I FALL 2020 Section 03 Computer Science I FALL 2020 Section 04 Computer Science I FALL 2020 Section 05 Computer Science I FALL 2020 Section 06 Computer Science I FALL 2020 Section 07 Computer Science I FALL 2020 Section 08 Computer Science I FALL 2020 Section 09 Computer Science I FALL 2020 Section 10 Computer Science I FALL 2020 Section 11 Computer Science I FALL 2020 Section 12 Computer Science I FALL 2020 Section 13 Computer Science I FALL 2020 Section 14 Computer Science I FALL 2020 Section 15 Computer Science I FALL 2020 Section 16 Computer Science I FALL 2020 Section 17 Computer Science I FALL 2020 Section 18 Computer Science I FALL 2020 Section 19 Computer Science I FALL 2020 Section 20 Computer Science I FALL 2020 Section 21 Computer Science I FALL 2020 Section 22 Computer Science I FALL 2020 Section 23 Data Structures FALL 2020 Section 01 Data Structures FALL 2020 Section 02 Data Structures FALL 2020 Section 03 Data Structures FALL 2020 Section 04 Data Structures FALL 2020 Section 05 Data Structures FALL 2020 Section 06 Data Structures FALL 2020 Section 07 Data Structures FALL 2020 Section 08 Data Structures FALL 2020 Section 09 Data Structures FALL 2020 Section 10 Foundations of Computer Science FALL 2020 Section 01 Foundations of Computer Science FALL 2020 Section 02 Foundations of Computer Science FALL 2020 Section 03 Foundations of Computer Science FALL 2020 Section 04 Foundations of Computer Science FALL 2020 Section 05 Introduction to Algorithms FALL 2020 Section 01 Introduction to Algorithms FALL 2020 Section 02 Introduction to Algorithms FALL 2020 Section 03 Introduction to Algorithms FALL 2020 Section 04 Introduction to Algorithms FALL 2020 Section 05 Introduction to Algorithms FALL 2020 Section 06 Computer Organization FALL 2020 Section 01 Computer Organization FALL 2020 Section 02 Computer Organization FALL 2020 Section 03 Computer Organization FALL 2020 Section 04 FALL 2020 Section 02 FALL 2020 Section 03 FALL 2020 Section 11 Machine Learning from Data FALL 2020 Section 01 Network Programming FALL 2020 Section 01 Frontiers of Network Science FALL 2020 Section 01 Networking in the Linux Kernel FALL 2020 Section 01 Ontologies FALL 2020 Section 01 Data Science FALL 2020 Section 01 Data and Society FALL 2020 Section 01 Database Systems FALL 2020 Section 01 Data Mining FALL 2020 Section 01 Programming Languages FALL 2020 Section 01 Software Design and Documentation FALL 2020 Section 01 Software Design and Documentation FALL 2020 Section 02 Software Design and Documentation FALL 2020 Section 03 Large-Scale Programming and Testing FALL 2020 Section 01 Robotics I FALL 2020 Section 01 Distributed Systems and Algorithms FALL 2020 Section 01 Numerical Computing FALL 2020 Section 01 Numerical Computing FALL 2020 Section 02 Topics in Computer Science FALL 2020 Section 01 Computational Linear Algebra FALL 2020 Section 01 Finite Element Analysis FALL 2020 Section 01 Computer Science Seminar FALL 2020 Section 01 Computer Science Seminar FALL 2020 Section 02 Master's Project FALL 2020 Section 03 Master's Project FALL 2020 Section 04 Master's Project FALL 2020 Section 05 Master's Project FALL 2020 Section 06 Master's Project FALL 2020 Section 07 Master's Project FALL 2020 Section 08 Master's Project FALL 2020 Section 09 Master's Project FALL 2020 Section 10 Master's Project FALL 2020 Section 11 Master's Project FALL 2020 Section 12 Master's Project FALL 2020 Section 13 Master's Project FALL 2020 Section 14 Master's Project FALL 2020 Section 16 Master's Thesis FALL 2020 Section 08 Master's Thesis FALL 2020 Section 09 Master's Thesis FALL 2020 Section 10 Master's Thesis FALL 2020 Section 11 Master's Thesis FALL 2020 Section 12 Master's Thesis FALL 2020 Section 13 Master's Thesis FALL 2020 Section 14 Master's Thesis FALL 2020 Section 15 Master's Thesis FALL 2020 Section 16 Master's Thesis FALL 2020 Section 17 Master's Thesis FALL 2020 Section 19 Master's Thesis FALL 2020 Section 20 Master's Thesis FALL 2020 Section 23 Master's Thesis FALL 2020 Section 27 Master's Thesis FALL 2020 Section 29 Master's Thesis FALL 2020 Section 30 Master's Thesis FALL 2020 Section 33 Master's Thesis FALL 2020 Section 34 Master's Thesis FALL 2020 Section 35 Master's Thesis FALL 2020 Section 36 Dissertation FALL 2020 Section 01 Dissertation FALL 2020 Section 03 Dissertation FALL 2020 Section 04 Dissertation FALL 2020 Section 05 Dissertation FALL 2020 Section 06 Dissertation FALL 2020 Section 07 Dissertation FALL 2020 Section 08 Dissertation FALL 2020 Section 09 Dissertation FALL 2020 Section 10 Dissertation FALL 2020 Section 11 Dissertation FALL 2020 Section 12 Dissertation FALL 2020 Section 13 Dissertation FALL 2020 Section 14 Dissertation FALL 2020 Section 16 Dissertation FALL 2020 Section 17 Dissertation FALL 2020 Section 18 Dissertation FALL 2020 Section 20 Dissertation FALL 2020 Section 21 Dissertation FALL 2020 Section 23 Dissertation FALL 2020 Section 24 Dissertation FALL 2020 Section 25 Dissertation FALL 2020 Section 26 Dissertation FALL 2020 Section 28 Dissertation FALL 2020 Section 29 Dissertation FALL 2020 Section 30 Dissertation FALL 2020 Section 31 Dissertation FALL 2020 Section 32 Dissertation FALL 2020 Section 33 Dissertation FALL 2020 Section 34 Dissertation FALL 2020 Section 37 Behavioral Neuroscience SUMMER 2020 Section 01 Cognition and the Brain SUMMER 2020 Section 01 SUMMER 2020 Section 01 Computer Science I SUMMER 2020 Section 01 Computer Science I SUMMER 2020 Section 02 Introduction to Algorithms SUMMER 2020 Section 01 Introduction to Algorithms SUMMER 2020 Section 02 Principles of Software SUMMER 2020 Section 01 Operating Systems SUMMER 2020 Section 01 Operating Systems SUMMER 2020 Section 02 Cryptography and Network Security I SUMMER 2020 Section 01 Numerical Computing SUMMER 2020 Section 01 Master's Project SUMMER 2020 Section 01 Master's Project SUMMER 2020 Section 02 Master's Project SUMMER 2020 Section 03 Master's Project SUMMER 2020 Section 04 Master's Project SUMMER 2020 Section 34 Master's Thesis SUMMER 2020 Section 01 Master's Thesis SUMMER 2020 Section 02 Master's Thesis SUMMER 2020 Section 04 Master's Thesis SUMMER 2020 Section 06 Master's Thesis SUMMER 2020 Section 08 Master's Thesis SUMMER 2020 Section 09 Master's Thesis SUMMER 2020 Section 10 Master's Thesis SUMMER 2020 Section 11 Master's Thesis SUMMER 2020 Section 16 Master's Thesis SUMMER 2020 Section 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