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Table of contents

While planning is a fundamental problem in artificial intelligence and decision making, robot planning refers to finding a path from A to B in the presence of obstacles and by complying with the kinematic constraints of the robot. In this course, algorithms will be implemented in Python on mobile platforms on ground and in the air. No prior experience with Python is needed but we require knowledge of data structures, linear algebra, and basic probability. The purpose of this course is to introduce undergraduate students in computer science and engineering to quantum computers QC and quantum information science QIS.

This course is meant primarily for juniors and seniors in CIS. No prior knowledge of quantum mechanics QM is assumed. Design and implementation of a significant piece of work: software, hardware or theory. In addition, emphasis on technical writing and oral communication skills. Students must have an abstract of their Senior Project, which is approved and signed by a Project Adviser, at the end of the second week of Fall classes.

The project continues during two semesters; students must enroll in CIS during the second semester. At the end of the first semester, students are required to submit an intermediate report and give a class presentation describing their project and progress. Grades are based on technical writing skills as per submitted report , oral presentation skills as per class presentation and progress on the project.

These are evaluated by the Project Adviser and the Course Instructor. Continuation of CIS Students are required to submit a final written report and give a final presentation and demonstration of their project. Grades are based on the report, the presentation and the satisfactory completion of the project. These are evaluated by the Project Advisor and the Course Instructor. Offered Spring Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others.

This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems. This course will introduce supervised learning decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning , unsupervised learning clustering, dimensionality reduction , and reinforcement learning.

Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis. Offered Fall and Spring Course Website. This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments will be in Python.

3rd Edition

Prerequisite s : level or higher biology courses, math ; introductory statistics. Probability theory and linear algebra highly recommended. The goal of this course is to develop a deeper understanding of techniques and concepts used in Computational Biology. The course will strive to focus on a small set of approaches to gain both theoretical and practical understanding of the methods.

We will aim to cover practical issues such as programming and the use of programs, as well as theoretical issues such as algorithm design, statistical data analysis, theory of algorithms and statistics. This course WILL NOT provide a broad survey of the field nor teach specific tools but focus on a deep understanding of a small set of topics.

We will discuss string algorithms, hidden markov models, dimension reduction, and machine learning or phylogeny estimation for biomedical problems. The goal of this course is to give students greater design and implementation experience in embedded software development and to teach them how to model, design, verify, and validate safety critical systems in a principled manner.

Information system - Wikipedia

Students will learn the principles, methods, and techniques for building life-critical embedded systems, ranging from requirements and models to design, analysis, optimization, implementation, and validation. Topics will include modeling and analysis methods and tools, real-time programming paradigms and languages, distributed real-time systems, global time, time-triggered communications, assurance case, software architecture, evidence-based certification, testing, verification, and validation.


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The course will include a series of projects that implements life-critical embedded systems e. This course provides an introduction to the broad field of database and information systems, covering a variety of topics relating to structured data, ranging from data modeling to logical foundations and popular languages, to system implementations.

This course focuses on the challenges encountered in building Internet and web systems: scalability, interoperability of data and code , security and fault tolerance, consistency models, and location of resources, services, and data. We will study techniques for locating machines, resources, and data including directory systems, information retrieval indexing, ranking, and web search ; and we will investigate how different architectures support scalability and the issues they face. An important goal of the course is not simply to discuss issues and solutions, but to provide hands-on experience with a substantial implementation project.

This course focuses on programming the essential mathematical and geometric concepts underlying modern computer graphics. Using 3D interactive implementations, it covers fundamental topics such as mesh data structures, transformation sequences, rendering algorithms, and curve interpolation for animation.

The curriculum is heavily project-based, and culminates in a group project focused on building an interactive first-person world exploration application using the various real-time interaction and rendering algorithms learned throughout the semester. Knowledge of vector geometry is useful. This course is designed to provide a comprehensive overview to computer graphics techniques in 3D modeling, image synthesis, and rendering. Topics cover: geometric transformations, geometric algorithms, software systems, 3D object models surface, volume and implicit , visible surface algorithms, image synthesis, shading, mapping, ray tracing, radiosity, global illumination, sampling, anti-aliasing, Monte Carlo path tracing, and photonmapping.

Prerequisite s : Previous exposure to major concepts in linear algebra i. This course covers core subject matter common to the fields of robotics, character animation and embodied intelligent agents. The intent of the course is to provide the student with a solid technical foundation for developing, animating and controlling articulated systems used in interactive computer games, virtual reality simulations and high-end animation applications.

Topics covered include: geometric coordinate systems and transformations; quaternions; parametric curves and surfaces; forward and inverse kinematics; dynamic systems and control; computer simulation; keyframe, motion capture and procedural animation; behavior-based animation and control; facial animation; smart characters and intelligent agents.

This is the second computer organization course and focuses on computer hardware design.

Projects in Computing and Information Systems: A Student's Guide eBook, 3rd Edition

Basic cache coherence and synchronization. Offered: Spring Course Website. Logic has been called the calculus of computer science as it plays a fundamental role in computer science, similar to that played by calculus in the physical sciences and traditional engineering disciplines. Indeed, logic is useful in areas of computer science as disparate as architecture logic gates , software engineering specification and verification , programming languages semantics, logic programming , databases relational algebra and SQL , artificial intelligence automatic theorem proving , algorithms complexity and expressiveness , and theory of computation general notions of computability.

CIS provides the students with a thorough introduction to mathematical logic, covering in depth the topics of syntax, semantics, decision procedures, formal proof systems, and soundness and completeness for both propositional and first-order logic. The material is taught from a computer science perspective, with an emphasis on algorithms, computational complexity, and tools.

Projects will focus on problems in circuit design, specification and analysis of protocols, and query evaluation in databases. The goal of this course is to provide an opportunity for seniors to define, design, and execute a project of your own choosing that demonstrates the technical skills and abilities that you have acquired during your 4 years as undergraduates.

Evaluation is based on selecting an interesting topic, completing appropriate research on the state of the art in that area, communicating your objectives in writing and in presentations, accurately estimating what resources will be required to complete your chosen task, coding necessary functionality, and executing your plan. Learn more about the NETS program here. Networked Life looks at how our world is connected — socially, economically, strategically and technologically — and why it matters.

Want to understand the sociological and algorithmic aspects of friend recommendation? Want to know how Google decides what 10 answers to return, out of the 10 million matching results? Want to understand how search engines have revolutionized advertising? Then this is the course for you! NETS provides an overview of the issues, theoretical foundations, and existing techniques in networks social, information, communication and markets on the Internet.

Subsequent NETS courses are available for students wishing to explore any of these topics in greater detail. Services must scale across thousands of machines, tolerate failures, and support thousands of concurrent requests. This course, aimed at a sophomore with exposure to basic programming within the context of a single machine, focuses on the issues and programming models related to such cloud and distributed data processing technologies: how to think about dividing both data and work across large clusters of machines, both within and across data centers, how to design algorithms that do this parallel computation, and how to implement the algorithms in new frameworks such as Spark and MapReduce.

Crowdsourcing and human computation are emerging fields that sit squarely at the intersection of economics and computer science. They examine how people can be used to solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms. Online marketplaces like Mechanical Turk and CrowdFlower provide an infrastructure that allows micropayments to be given to people in return for completing human intelligence tasks.

This opens up previously unthinkable possibilities like people being used as function calls in software. What You will Learn about. We will investigate how crowdsourcing can be used for computer science applications like machine learning, next-generation interfaces, and data mining. Beyond these computer science aspects, we will also delve into topics like prediction markets, how businesses can capitalize on collective intelligence, and the fundamental principles that underlie democracy and other group decision-making processes.

Want to understand how memes spread across the Internet? How organisms exhibit flocking behavior? How the structure of a network can help predict behavior among the nodes?