COMPUTER SCIENCE (CSCI)

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• 1. Demonstrate how data is represented in computers
• 2. Define the building blocks and organization of computer hardware
• 3. Describe the differences between machine, assembly, and high-level languages
• 4. Design an efficient algorithm and analyze its time/space complexity
• 5. Implement algorithms that solve problems
• 6. Explain how the Internet works
• 7. Detail how an operating system manages processes
• 8. Evaluate whether one is keeping data private
• 9. Categorize the different threats to computer systems
• 10. Perform the data science process
• 11. Explain the Turing test and how one defines intelligence
• 12. Categorize the different types of machine learning algorithms
• 13. Describe key computer ethics topics
• 14. Recognize the impact of computing on the world
• 15. Design and program Python applications,
• 16. Write loops and decision statements in Python,
• 17. Use lists, tuples, sets, dictionaries, and strings in Python,
• 18. Define the structure/components of a Python program,
• 19. Write functions and pass arguments in Python,
• 20. Write code that reads/writes files in Python,
• 21. Design object-oriented programs with Python classes, and
• 22. Read (trace) basic Python code.

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• Design and program Python applications
• Write loops and decision statements in Python
• Use lists, tuples, sets, dictionaries, and strings in Python
• Define the structure/components of a Python program
• Write functions and pass arguments in Python
• Write code that reads/writes files in Python
• Design object-oriented programs with Python classes
• Read (trace) basic Python code

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• Analyze a simple empirical problem, break it down into smaller more manageable components, and design algorithmic solutions to these subproblems
• Implement an existing prompt, plan, or design into programmatically correct Python code that produces the expected text, file, or graph output
• Communicate in the language of programming with a computer and other programmers through code reading, writing, and critique
• Critically discuss and reflect on the role technology has in modern society, and the positive and negative impacts their software may have on future users
• Model how basic numeric and non-numeric data is represented in a computer; identify when, why, and how these physical representations diverge from their conceptual equivalents
• Navigate and utilize a computer file system through a GUI, the text console, and code
• Demonstrate effective debugging practices in an IDE to find, characterize, and correct code errors

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• 1. Describe a breadth of foundational computer science topics and how they relate to emphasis areas in computing.
• 2. Implement networking and relationship building techniques within their cohort and other like-minded students.
• 3. Explain the importance of personal development tools and preparedness for careers in computing (e.g., imposter syndrome, the elevator pitch, resume building, navigating career day).
• 4. Summarize the different types of careers that exist in the computer science field.
• 5. Analyze the specific fields in computer science that are of most interest for deeper exploration and potential degree focus area.

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• L1. Design an algorithm to solve a problem by breaking the overarching problem into smaller modular components using abstraction and object-oriented design with inheritance.
• L2. Translate the algorithm into a program using proper C++ syntax and fundamental programming constructs (e.g. control structures, I/O, classes).
• L3. Recite & apply frequently used Linux command line commands and compile a program using a command line build system.
• L4. Diagram memory usage, dynamically allocate & deallocate objects at run-time using "The Big Three," and trace the call stack of a program’s run-time.
• L5. Define recursion and construct common recursive data structures (e.g. linked list, stack, queue) & algorithms (e.g. search & sort).
• L6. Diagram & construct dynamically allocated data structures (e.g. array, vector, string), recursive data structures, (e.g. linked list, stack, queue), and implement common list operations (e.g. traversal, insertion, removal)
• L7. Define "Big-O" notation, list complexities in increasing order, and analyze an algorithm to compute its run-time performance & memory complexities

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• 1. Identify, select, and apply appropriate Linux commands for file, process, and network management.
• 2. Utilize the command line by designing and creating shell scripts; compiling and linking programs; redirecting input and output of processes; and executing commands for controlling jobs/processes.
• 3. Explain the purpose of and apply advanced SSH functions such as port forwarding, dynamic proxying, effectively.
• 4. Administer a Linux system applying knowledge of the file system hierarchy, package management, kernel compilation, and network management.
• 5. Design and write C programs composed of dynamic memory management, function pointers, c-style polymorphism, recursive functions, and data structures.
• 6. Examine the capabilities of a code repository maintenance tool, such as git, through command line version control.
• 7. Select and implement appropriate security, privacy, and encryption protocols at the operating system level.
• 8. Design and implement programs with client-server architecture using inter-process communication.
• 9. Discuss and apply appropriate time and project management strategies through the development of multiple substantial programming projects throughout the semester.

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• Apply algorithmic analysis methods including solving recurrence relations, the “unifying theorem,” and amortized analysis to compute best case, worst case, and average case run-time complexity of algorithms and operations on data structures; and discuss trade-offs between time and space complexity.
• Describe hash tables and write software implementing a hash table; explain and evaluate collision handling methods in the context of storage hierarchies; and assess the relative merits of different hash functions for data types such as strings and integers.
• Describe search trees and their applications and write software implementing a search tree; explain and contrast trees such as red-black or AVL trees, splay trees, b-trees, and radix tries in application contexts such as operating systems, network routing, and databases.
• Describe heaps and priority queues and write software implementing a heap; apply priority queues to problem domains such as data compression and operating systems.
• Define unweighted, weighted, undirected, and directed graphs and explain data structures for storing graphs; explain and analyze algorithms for graph discovery and classification.
• Explain and analyze algorithms on graphs for finding shortest paths and minimum spanning trees; write software implementing graph algorithms; discuss applications of graph algorithms.
• Demonstrate professional conduct with respect to communication and time management.

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• 1. Create, navigate, and manage files and directory structures using basic Linux shell commands.
• 2. Describe the functionality and purpose of the individual components of the Raspberry Pi Hardware.
• 3. Install the Raspian operating system onto the Raspberry Pi Hardware and setup basic configuration parameters.
• 4. Download, install, and develop programs using the Spyder Integrated Development Environment (IDE) on the Raspberry Pi Hardware.
• 5. Develop and run basic Python functions and programs in the Linux environment to collect data from sensors using the Raspberry Pi Hardware (e.g., acoustic, acceleration, magnetic field, optical).
• 6. Plot and analyze data from the sensor system and compare to mathematical models.

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• Solve common programming problems and identify underlying patterns
• Argue and prove correctness of solution
• Determine and argue space and time complexity of solutions
• Combine common algorithms to solve problems
• Improve skills in technical interviews and programming competitions

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• 1. Understand different types of companies that hire CS majors through their research and visits with the companies
• 2, Have answers to questions they have generated, to further their understanding of positions and roles in CS industry
• 3. Improve their professional communication and networking skills through experience
• 4. Shape their future career path by relating what they learn in the course

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• 1. Acquire, clean, and organize data from a variety of sources, including raw data files, SQL databases, and online repositories.
• 2. Run simple SQL queries to manipulate and analyze data in relational databases and other data stores implementing SQL front ends.
• 3. Apply toolkits to preprocess large datasets for input to statistical and machine learning algorithms, including methods of feature extraction and dimensionality reduction.
• 4. Apply statistical and machine learning toolkits to large datasets, including applications of regression, classification, and clustering.
• 5. Perform simple visualizations of data.
• 6. Recognize and address the ethical issues arising from data collection and statistical and machine learning.

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• Articulate the nature and purpose of Software Engineering along with the three major goals of Software Engineering.
• Demonstrate ability to quickly learn unfamiliar programming languages and characterize common traits of OOP across languages.
• Describe and utilize various agile and extreme programming methodologies.
• Design complex soft ware architecture using UML.
• Design and develop quality soft ware using specific methodologies including TDD, Customer Story Requirements, Coding Standards, Code Review, Pair Programming, and Refactoring.
• Analyze OOP architecture within the context of Software Principles and Software Design Patterns to effectively strengthen software quality.
• Demonstrate effective use of various version control/configuration management tools in a development environment.
• Effectively analyze and debug software using various debugging techniques, tools, and methodologies.
• Function effectively in a software development team environment.

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• Program in RISC-V assembly language
• Translate between C programs and RISC-V assembly code
• Translate between RISC-V assembly code and RISC-V machine code
• Represent a number using IEEE 754 floating point standard
• Describe how a CPU performs addition, subtraction, multiplication, and division
• Evaluate CPU performance
• Design a single-cycle processor and its control
• Design pipelining techniques to speed up a single-cycle processor
• Elaborate how memory hierarchy works

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• Think logically and mathematically.
• Understand mathematical reasoning.
• Perform combinatorial analysis.
• Represent discrete objects and relations using discrete structures.
• Model problems in diverse areas with discrete math.

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• unchanged

View Course Learning Outcomes

• Solve common programming problems and identify underlying patterns
• Argue and prove correctness of solution
• Determine and argue space and time complexity of solutions
• Combine common algorithms to solve problems
• Improve skills in technical interviews and programming competitions

View Course Learning Outcomes

• 1. Understand different types of companies that hire CS majors through their research and visits with the companies
• 2. Have answers to questions they have generated, to further their understanding of positions and roles in CS industry
• 3. Improve their professional communication and networking skills through experience
• 4. Discover different company cultures / work environments
• 5. Shape their future career path by relating what they learn in the course

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• Design and implement program in a functional style in a functional language
• Implement the core of a programming language
• Explain programming language theory terms including lexical closure, lexing, parsing, operational semantics, lexical environment, type checking, type environment, type inference, and references;
• Relate common programming constructs and idioms to the lambda calculus
• Reason inductively about recursive functions and data structures;
• Analyze, compare, and contrast the suitability of mutable/ephemeral/imperative vs. immutable/persistent/functional programming approaches and data structures for new problems

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• (Data Modeling and Database Design) Identify data elements and relationships between data elements required by an application. Design and implement an appropriate data representation for data and relationships using correct application of types, constraints, and keys. Justify the design decisions made in the data model. Illustrate the data model using Entity-Relation Diagrams.
• (Queries) Construct relational queries that produce precise and complete answers to questions about the data. Compose queries using advanced SQL features including Common Table Expressions, window functions, and User-Defined Functions. Evaluate correctness and efficiency of queries and evaluate tradeoffs between queries using different techniques such as joins, subqueries, and different methods of aggregation.
• (Performance Analysis and Tuning) Analyze database performance and tune the design and configuration of the database to improve query and insert performance. Propose, implement, and justify performance tuning decisions using indexes, materialized views, and other techniques.
• (Transactional data) Construct data modification statements on a relational database. Design transaction-based data updates for applications and select appropriate locking strategies under different application scenarios.
• (Normalization) Analyze data dependencies, formulate functional dependencies, and normalize a relational database schema. Explain the benefits of normalization in the context of transactional data applications. Assess tradeoffs and benefits of denormalization in different contexts.
• (Software engineering) Design and implement a software application which uses a relational database to manage data. Justify the design decisions made in the application, including when to process data through SQL and when to process data in the application. Identify security risks in application code and apply security best practices to prevent common security problems.
• (Non-relational models) Explain how NoSQL, Key-Value, Document, and Graph databases differ from relational databases and when to use them for different applications. Evaluate tradeoffs between different data representations.
• (Data engineering) Identify data cleaning, inspection, and other pre-processing steps for noisy structured and semi-structured datasets, and apply these steps within a relational database system. Demonstrate bulk data loading and extract-transform-load within a relational database system.

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• Identify problems where artificial intelligence techniques are applicable.
• Apply selected basic AI techniques, judge applicability of more advanced techniques.

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• Reasoning about algorithm correctness (proofs, counterexamples)
• Analysis of algorithms

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• 1. Conduct data acquisition using a varied set of techniques with structured and unstructured datasets; including raw data files, SQL databases, online repositories, and programmatically through web scraping and APIs.
• 2. Apply preprocessing strategies to complex and dynamic datasets using industry-standard toolkits and machine learning algorithms to extract features, reduce dimensionality, remove errors, inconsistencies, and missing values.
• 3. Differentiate between machine learning approaches such as classification, regression, clustering, and neural networks for predictive analytics and pattern recognition.
• 4. Evaluate the predictive power of the different statistical and machine learning methods to solve real-world data science problems.
• 5. Develop storytelling and visualization techniques to effectively communicate (exploratory) or persuade (explanatory) findings to a specific audience.
• 6. Critically assess ethical considerations and challenges related to data collection and analysis.
• 7. Construct a comprehensive data science project from inception to presentation, integrating the various techniques and tools learned throughout the course.

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• 1. Ideate and propose interaction design problems
• 2. Explain and apply core theories and models from the field of HCI
• 3. Create task-focused scenarios to guide design efforts, and use sketches and storyboards to communicate those scenarios
• 4. Improve existing designs through rapid prototyping and iteration
• 5. Communicate a design, and justify design decisions, through writing and spoken presentations
• 6. Read, discuss and critique research in the field of HCI

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• Simulation software design and implementation.
• Individual "Student Bounties" in simulation, results of journaled communications.
• Design and develop software applications, applying effective software engineering practices and algorithmic techniques to maximize efficiency.
• Select or develop appropriate Abstract Data Types (ADTs) to solve realistic problems. Analyze the complexity of algorithms used in problem solutions.
• Display effective professional and interpersonal skills such as communication, teamwork, and knowledge of ethical issues specific to computing.

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• 1. Understand the steps taken by most compilers to translate a program listing to either another language or target code for a machine.
• 2. Understand and be able to apply the theory of lexical analysis, regular expressions, deterministic finite automata (DFAs), non-deterministic finite automata (NFAs), and how to use scanner generators (eg flex).
• 3. Know the fundamental principles of context free grammars as well as their associated attributes and properties.
• 4. Understand the constructs for expressing operator precedence and associativity in context free grammars, as well as how language ambiguities are detected and worked around by modern compiler tools.
• 5. Know the theory and implementation of top-down and bottom up parsing, the generation of parse trees, and their translation to abstract syntax trees (ASTs).
• 6. Know program statements are translated to machine language at the CPU op-code and register level.

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• 1. Understand the basic ethical theories, concepts, tools, and frameworks for analyzing the social and ethical ramifications of robotics
• 2. Be able to critically examine the ethical significance of the use of robotics in daily and technical fields including human-robot interaction, medicine, relationship, military, etc.
• 3. Develop a critical attitude toward the role of robotics in shaping human society including human perceptions and behaviors
• 4. Be able to use the theories, concepts, tools, and frameworks learned from this class to critically examine emerging robot ethics issues in the society.
• 4. Understand the tradeoffs underlying the design of autonomous moral agents

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• 1. Understand the theoretical foundations and critical application domains of human-robot interaction.
• 2. Employ design techniques to design interactive robots.
• 3. Design human-subject experiments to evaluate interactive robots.
• 4. Perform qualitative and quantitative analysis on the results of human-robot interaction experiments.

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• Be able to analyze and predict the behavior of image formation, transformation, and recognition algorithms.
• Be able to design, develop, and evaluate algorithms for specific computer vision applications.
• Be able to use software tools (such as OpenCV) to implement computer vision algorithms.
• Develop an understanding of classical (hand-engineered) and modern (deep-learning-based) computer vision algorithms.
• Learn key algorithms in 3 major areas of image filtering, camera geometry, and machine learning.

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• To follow

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• Describe, replicate, & modify the graphical pipeline
• Create interactive, real-time graphics applications

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• Learn the essential concepts in the design and implementation of operating systems: what they can do, what they contain, and how they are implemented.
• Sharpen C/C++ programming and object-oriented development skills and practice on simulation software design and implementation and get familiar with using Linux system calls.
• Understand fundamental resource management and scheduling techniques that will help you establish the necessary base-knowledge required in many CS-related fields of industry and research: performance optimization, security considerations, resource sharing, etc.

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• Describe, replicate, and modify the modern advanced graphics pipeline
• Create a real-time graphics application using an advanced graphics technique

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• Design and develop a modern website using HTML5 + CSS3 while applying software engineering principles.
• Discuss and apply proper design methodologies to create visually appealing and accessible websites.
• Describe the basic structure of the Internet and World Wide Web. Discuss how a URL determines what webpage is returned to a browser from this structure.
• Explain and compare the differences between (1) client-side and server-side processing (2) front- end development and back-end development.
• Create a responsive, dynamic, and interactive website using JavaScript.
• Create a responsive, dynamic, and database backed website using Go.
• Create a responsive, dynamic, and interactive website using AJAX and HTMX.
• Locate information and construct a solution to learn new technologies/languages/tools/libraries on your own.

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• In the context of a problem that the student sets out to solve, synthesize the pain of target users through conducting user interviews.
• Create wireframes based on insights from user interactions.
• Develop and launch a working beta product based on user needs.
• Engage in a feedback loop with users to refine their product and drive user adoption.
• Foster high performance in teams by developing proficiency in feedback mechanisms.
• Develop mastery of storytelling in the context of software startup ventures.

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• 1. Understand the basic ideas of supervised learning, unsupervised learning and reinforcement learning, as well as their applications to real-world problems.
• 2. Use the linear models for regression and classification and write programs to implement these models using off-the-shelf packages.
• 3. Understand the kernel methods to deal with nonlinear problems and use support vector machines and neural networks to solve real-world problems.
• 4. Understand unsupervised learning and solve clustering and dimensionality reduction problems.
• 5. Understand reinforcement learning and its applications in robotics.

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• using TCP/IP standardized protocols

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• 1. Explain the basic concepts in human-centered robotics
• 2. Model and analyze human behaviors for human-robot interaction applications
• 3. Recognize the cutting-edge human-centered robotics research and applications
• 4. Apply the learned knowledge to other fields

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• Explain the basic terms and concepts in cryptography.
• Correlate number theory with cryptography.
• Analyze the limitations of classical cryptographic algorithms.
• Compare the similarities and differences between symmetric and asymmetric encryption algorithms.
• Assess the security strength of cryptographic techniques.
• Choose appropriate cryptographic techniques such as hashing, message authentication, and digital signature for addressing real-world security and privacy problems.
• Critique research work in applied cryptography.

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• Explain the fundamental security and privacy protection principles, techniques, and practices.
• Describe risk mitigation, legal requirements, and ethical considerations in cybersecurity.
• Analyze real-world security and privacy problems in modern computing environments.
• Practice security and privacy protection tools and techniques.
• Critique existing security and privacy protection techniques or studies.

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• Define the process of game software design and development from start to finish.
• Develop a game design document and project plan to be utilized in the development and implementation of a game.
• Develop a working game product using an existing game engine.
• Write game programming code and scripting.
• Identify the core types of AI behavior and their uses, such as pathfinding, fuzzy logic, cooperative behavior, decision trees, neural nets, adaptive and heuristics.
• Discuss the importance of story, character, actions, and rewards as part of a successful game design.
• Implement story, character, gameplay, challenges, and mechanics in the design of a game.

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• Construct a systems level abstraction of the dynamics of a cell by relating components of the cell such as genes, proteins, and metabolic reactions.
• Design and implement algorithms for solving pairwise, multiple sequence alignments, and search sequence databases using sequences from NCBI GenBank or Uniprot.
• Design and implement algorithms for constructing phylogenetic trees for a set of biological sequences and apply them on real datasets on sequence families, such as SARS-CoV-2 variants.
• Apply existing tools to predict protein structures from sequence and compare multiple protein structures using sequences from Uniprot and structures from the Protein Data Bank.
• Interpret and analyze transcriptome data such as microarray data or RNA-Seg data to reveal clusters of genes/samples and identify differentially expressed genes.
• Design and implement algorithms for analyzing biological networks such as protein-protein interaction networks, gene-regulatory networks, and metabolic networks.

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• TBA

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• Solve common programming problems and identify underlying patterns
• Argue and prove correctness of solution
• Determine and argue space and time complexity of solutions
• Combine common algorithms to solve problems
• Improve skills in technical interviews and programming competitions

View Course Learning Outcomes

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• 1. Be able to analyze and predict the behavior of image formation, transformation, and recognition algorithms
• 2. Be able to design, develop, and evaluate algorithms for specific applications
• 3. Be able to use software tools to implement computer vision algorithms
• 4. Communicate (in oral and written form) methods and results to a technical audience

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• 1. Be able to review the literature on computer vision and create a critical review
• 2. Be able to design, develop, and evaluate algorithms for specific applications
• 3. Be able to use software tools to implement computer vision algorithms
• 4. Communicate (in oral and written form) methods and results to a technical audience

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• Develop and maintain a project management plan to address a software engineering design challenge that includes a clear definition of done and detailed project schedule.
• Formulate a clear, concise set of correctly constructed engineering metrics/constraints and specifications for a software/hardware engineering design challenge.
• Develop and graphically communicate multiple innovative high-level design solutions to a given software/hardware engineering challenge.
• Utilize decision methods to select the most promising solutions(s) that meet(s) constraints and requirements for the given design challenge.
• Develop a proof-of-concept protype(s) for the critical and high-risk functional elements of the design challenge.
• Develop a project completion plan and execute Agile project development methodology that allows the team to successfully meet the project goals.
• Apply appropriate technical knowledge to solve a design challenge.
• Communicate your engineering design, research, analysis, decision making and solution via professional, technical, and industry appropriate written documentation packages.
• Communicate technical information verbally as part of a sprint or design review and synthesize feedback from stakeholders and clients.
• Identify and analyze the ethical, environmental, societal and/or economic impact of considered design solutions.
• Function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks and meet design challenge objectives.
• Evaluate your success as a designer by reflecting on your experience and proposing strategies for future self-improvement.

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• Design an engineering solution for your given design challenge within constraints.
• Demonstrate design feasibility through engineering analysis.
• Identify project and/or technical risks and develop a proactive risk mitigation plan.
• Test and analyze a solution.
• Deploy a solution into the environment within constraints.
• Develop a project completion plan and execute Agile project development methodology that allows the team to successfully meet the project goals.
• Apply appropriate technical knowledge to solve a design challenge.
• Communicate your engineering design, research, analysis, decision making and solution via professional, technical, and industry appropriate written documentation packages.
• Communicate technical information verbally as part of a sprint or design review and synthesize feedback from stakeholders and clients.
• Identify and analyze the ethical, environmental, societal and/or economic impact of considered design solutions.
• Function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks and meet design challenge objectives.
• Evaluate your success as a designer by reflecting on your experience and proposing strategies for future self-improvement.

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• 1 - Understand the basic ethical theories, concepts, tools, and frameworks for analyzing the social and ethical ramifications of robotics
• 2 - Be able to critically examine the ethical significance of the use of robotics in daily and technical fields including human-robot interaction, medicine, relationship, military, etc.
• 3 - Develop a critical attitude toward the role of robotics in shaping human society including human perceptions and behaviors
• 4 - Be able to use the theories, concepts, tools, and frameworks learned from this class to critically examine emerging robot ethics issues in the society.
• 5 - Understand the tradeoffs underlying the design of autonomous moral agents.
• 6 - Conduct robot ethics research grounded in both human-subject experimentation and algorithm development.

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• 1 - Implement algorithms for symbolic computation
• 2 - Construct symbolic planning domains for new scenarios
• 3 - Implement algorithms for symbolic planning via constraint-solving and heuristic search
• 4 - Implement algorithms for sampling-based motion planning
• 5 - Construct kinematic models of robot manipulators
• 6 - Analyze planning algorithms for key properties: correctness, completeness, optimality
• 7 - Evaluate the suitability of different planning approaches and apply appropriate algorithms to new planning scenarios
• 8 - Communicate implementations, analysis, and evaluation in written and oral form

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• Understand the theoretical foundations and critical application domains of human-robot interaction.
• Employ design techniques to design interactive robots.
• Design human-subject experiments to evaluate interactive robots.
• Perform qualitative and quantitative analysis on the results of human-robot interaction experiments.

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• At the completion of the course, you will be able to:

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• 1. Understand the basic concepts in game theory
• 2. Be able to model and analyze real-world problems as games
• 3. Learn about the game theoretic applications in networks
• 4. Have the opportunity to apply game theory to other fields

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• 1) Explain how packet switching works
• 2) Compute and identify potential packet delays in a packet-switched network when provided specific parameters of network conditions.
• 3) Articulate in laymen’s terms the following concepts or processes: a) essential principles of a transport layer protocol (reliable data transfer, flow control, congestion control), b) data link layer services and multiple access techniques, c) how mobility is managed in cellular networks, and d) network security issues and provide examples of a few methods that address them
• 4) Apply routing algorithms to find shortest paths for network-layer packet delivery
• 4) Apply routing algorithms to find shortest paths for network-layer packet delivery
• 5) Apply Wireshark, a network sniffing tool, to observe and analyze behaviors of the networking protocols studied in this course
• 6) Design and implement distributed applications using the socket APIs and support for TCP/UDP communications between end hosts

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• Design and implement various types of automata, including finite automata, pushdown automata, and Turing machines.
• Model and solve computational problems using formal language representations and algorithms.
• Communicate effectively using mathematically precise terminology, particularly about topics in language and complexity theory.
• Formally prove capabilities and limits, such as decidability and complexity of new problems, based on computational classes.
• Evaluate potentially effective (or ineffective) approaches for new problems based on language and complexity class.
• Apply reduction techniques to show the equivalence between different computational problems.

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• Communicate effectively on matters related to algorithms and data structures to a range of audiences using a range of media.
• Solve moderately open-ended problems related to graphs, NP completeness, approximation algorithms, and geometric algorithms.
• Demonstrate attention to detail (with respect to definitions, terminology, notation, language) as measured by performance on multiple-choice and true-false questions on online-quizzes.
• Execute a sequence of increasingly difficult (but guided) research related tasks related to an important application area that requires.
• Function effectively as a team member with effectiveness being determined by peer ratings, self-assessment and instructor observations.

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• Develop parallel programs to solve complex scientific problems using both shared memory and distributed memory models.
• Analyze and optimize the performance of parallel algorithms to achieve efficient computational solutions.
• Classify and differentiate between various parallel computing architectures and understand the associated hardware and software challenges.
• Gain hands-on experience with high-performance supercomputers by writing, testing, and debugging parallel applications over networked environments.

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• Understand quantitative methods to evaluate system performance, including metrics and benchmarks.
• Analyze the design and optimization of memory hierarchies, including caches and virtual memory.
• Understand CPU pipelines, focusing on throughput and instruction scheduling.
• Understand the techniques for speculative execution and branch prediction.
• Understand the GPU architecture.

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• Analyze complexity of distributed algorithms.
• Prove correctness of distributed algorithms.
• Implement virtual clocks.
• Implement and evaluate distributed minimum spanning tree algorithms.
• Implement distributed mutual exclusion algorithms.
• Compare and contrast distributed leader election algorithms.
• Implement different data-centric and client-centric consistency models.
• Review recent distributed systems research.

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• Articulate wireless networking challenges.
• Describe wireless MAC layer protocols.
• Describe the design of WiFi networks and Cellular networks.
• Use network sniffing tools to characterize the impact of physical environments on wireless signal propagation.
• Use a network simulator to evaluate the performance of routing protocols for wireless ad-hoc networks.
• Analyze WiFi's MAC layer protocol in different scenarios using a network simulator.
• Review recent wireless networking research.

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• 1. Explain the basic concepts in human-centered robotics
• 2. Model and analyze human behaviors for human-robot interaction applications
• 3. Recognize the cutting-edge human-centered robotics reasearch and applications
• 4. Apply the learned knowledge to other fields

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• Explain the basic terms and concepts in cryptography.
• Correlate number theory with cryptography.
• Analyze the limitations of classical cryptographic algorithms.
• Compare the similarities and differences between symmetric and asymmetric encryption algorithms.
• Assess the security strength of cryptographic techniques.
• Choose appropriate cryptographic techniques such as hashing, message authentication, and digital signature for addressing real-world security and privacy problems.
• Critique research work in applied cryptography.

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• Understand the different types of machine learning and some popular methods in each.
• Be able to use machine learning methods in real-world applications.
• Be able to determine applicable machine learning techniques for different datasets.
• Understand how to use machine learning to learn features from high dimensional or complex data.
• Understand the ethical implications of machine learning models.

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• Apply supervised, unsupervised, reinforcement machine learning models and deep learning models to solve problems in areas such as prediction, recognition and classification.
• Explore and develop with various tools, techniques and libraries in Python for data processing, feature extraction, visualization, validation and evaluation.
• Create data visualization tools, techniques, and libraries in Python to visualize high dimensional or complex data for stakeholders
• Determine ethical implications through interpretability of big data and results from the application of various machine learning models
• Design and develop a machine learning product that solves their chosen real-world challenge
• Create a video presentation that succinctly outlines the problem, solutions, conclusions, and lessons learned regarding product development for the stakeholders

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• Construct a systems level abstraction of the dynamics of a cell by relating components of the cell such as genes, proteins, and metabolic reactions
• Design and implement algorithms for solving pairwise, multiple sequence alignments, and search sequence databases using sequences from NCBI GenBank or Uniprot
• Design and implement algorithms for constructing phylogenetic trees for a set of biological sequences and apply them on real datasets on sequence families, such as SARS-CoV-2 variants
• Apply existing tools to predict protein structures from sequence and compare multiple protein structures using sequences from Uniprot and structures from the Protein Data Bank
• Interpret and analyze transcriptome data such as microarray data or RNA-Seq data by to reveal clusters of genes/samples and identify differentially expressed genes
• Design and implement algorithms for analyzing biological networks such as protein-protein interaction networks, gene-regulatory networks, and metabolic networks

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• Develop skills to design, implement, and debug parallel programs using frameworks like MPI and OpenMP.
• Learn to analyze and optimize computational performance on multi-core and distributed systems.
• Understand and apply algorithms that efficiently scale across high-performance computing architectures.
• Gain familiarity with HPC tools, libraries, and systems to solve complex computational problems effectively.

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• Building on the education received in Fundamentals of Quantum Information, students will learn:
• 1. How to write and implement simple quantum algorithms,
• 2. Understand mechanisms by which a quantum speedup can be obtained
• 3. Use and execute code on an API of publicly available quantum hardware (e.g. IBM’s qiskit, Rigetti’s Forest, Google’s forthcoming Cirq, and many more)
• 4. “Debug” their code to improve its performance given the realities of noise and gate error

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• Explore the principal design concepts of domain-specific accelerators, contrast them to traditional CPU architectures, understand how they are programmed, and comprehend their role and importance in modern computing.
• Improve critical thinking and analysis abilities on accelerated computing research and practice technical presentation of concepts and papers in both spoken and written forms.
• Measure and analyze performance of the state-of-the-art system-on-chips for autonomous and mobile computing using industry-standard parallel-programming languages, paradigms, and APIs.

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• Develop a comprehensive understanding of the security and privacy challenges posed by IoT devices.
• Approach these issues in a data-driven and evidence-based manner.
• Read, understand, present, and critique IoT academic papers.
• Analyze, formulate and address IoT security and privacy research problems.
• Master cutting-edge IoT data analysis techniques using AI models (e.g., generative and embedded AI).

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• Explain the fundamental security and privacy protection principles, techniques, and practices.
• Describe risk mitigation, legal requirements, and ethical considerations in cybersecurity.
• Analyze real-world security and privacy problems in modern computing environments.
• Practice security and privacy protection tools and techniques.
• Critique existing security and privacy protection techniques or studies.

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• 1. Students will be able to explain the essential security and privacy protection requirements in representative Cyber Physical Systems.
• 2. Students will be able to analyze security and privacy problems in representative Cyber Physical Systems.
• 3. Students will be able to analyze and evaluate research articles in Cyber Physical Systems security and privacy protection.
• 4. Students will be able to design usable solutions for addressing security and privacy problems in representative Cyber Physical Systems.