Students will learn to analyze and mitigate privacy loss, unfairness, and lack of statistical validity, in data analysis. Principal techniques will come from cryptography, differential privacy, and the newly emerging areas of adaptive data analysis and fairness in machine learning. Cryptography is as old as human communication itself, but has undergone a revolution in the last few decades. It is now about much more than "secret writing" and includes seemingly paradoxical notions such as communicating securely without a shared secret, and computing on encrypted data.
In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as fully homomorphic encryption and software obfuscation. This is a proof-based course that will be best appreciated by mathematically mature students. Networks—of social relationships, economic interdependencies, and digital interactions—are critical in shaping our lives.
This course introduces models and algorithms that help us understand networks. Applications discussed include the viral spread of ideas, maximizing influence, and the contagion of economic downturns.
Concepts and tools covered include game theory, graph theory, data mining, and machine learning. The interplay between economic thinking and computational thinking as it relates to electronic commerce, social networks, collective intelligence and networked systems. Topics covered include: game theory, peer production, reputation and recommender systems, prediction markets, crowd sourcing, network influence and dynamics, auctions and mechanisms, privacy and security, matching and allocation problems, computational social choice and behavioral game theory. Emphasis will be given to core methodologies, with students engaged in theoretical, computational and empirical exercises.
This course introduces fundamentals in designing and building modern information devices and systems that interface with the real world. It focuses on digital devices and systems, and it complements ENG-SCI , which focuses on devices and systems that use analog electronics. Topics include: combinational and sequential logic; computer architecture; machine code; and altogether the infrastructure and computational framework composing a MIPS processor. Consideration is given in design to interactions between hardware and software systems.nonamepos.myerp.work/erp/lycixusob/2291-pais-de-mujeres.php
Computer Science (CS)
Students will design application specific hardware for an embedded system. Computer networking has enabled the emergence of mobile and cloud computing, creating two of the most important technological breakthroughs in computing of the past decade. We expect three major focuses in the next ten years. First, we will witness the emergence of 5G wireless mobile networks, which will begin to replace the current 4G networks as early as , enabling new applications and paradigm shifts in edge computing, such as uploading sensor data for AI applications everywhere.
Second, cyber security, and in particular its relation to networking and supply chain security for 5G network infrastructure, will receive unprecedented attention from industry. Third, blockchain technology, which has powered Bitcoin, is creating a new trusted network infrastructure that will allow information to be distributed but not copied.
While these areas are each rich in intellectual content on their own, they will also interplay with one other, creating interesting opportunities for those versed in all three. In order to play a role in this era of network-based computing, students must have a thorough understanding of these networking technologies and applications.
Beyond teaching the basic networking protocols, which have become very mature and can be treated as a black box, in CS , we will teach new networking issues and topics of significance. This focus on upcoming wireless, cyber security as it relates to networks, network infrastructure, and the broader supply chain, and blockchain advancements is the motivation for CS this semester. Students in the course will read and discuss basic material as well as the latest literature, work on homework assignments, gain hands-on experience through network programming, and have the opportunity to present the concepts and insights learned through a final project.
Clouds have become critical infrastructures for many applications in business and society e.
Readings in Computer Vision - 1st Edition
In this course, we will take a look inside the cloud infrastructure and learn critical technology trends and challenges in the networking and computing layers. We will discuss the design choices of performance, scalability, manageability, and cost in various cloud companies such as Amazon, Google, Microsoft, and Facebook. This course includes lectures and system programming projects. Review of the fundamental structures in modern processor design. Topics include computer organization, memory system design, pipelining, and other techniques to exploit parallelism.
Emphasis on a quantitative evaluation of design alternatives and an understanding of timing issues. Topics include: basic semiconductor theory; MOS transistors and digital MOS circuits design; synchronous machines, clocking, and timing issues; high-level description and modeling of VLSI systems; synthesis and place and route design flows; and testing of VLSI circuits and systems.
Various CAD tools for design, simulation, and verification are extensively used. Comprehensive introduction to the principal features and overall design of both traditional and modern programming languages, including syntax, formal semantics, abstraction mechanisms, modularity, type systems, naming, polymorphism, closures, continuations, and concurrency.
Provides the intellectual tools needed to design, evaluate, choose, and use programming languages. Implementation of efficient interpreters and compilers for programming languages.
Associated algorithms and pragmatic issues. Emphasizes practical applications including those outside of programming languages proper.
Also shows relationships to programming-language theory and design. Participants build a working compiler including lexical analysis, parsing, type checking, code generation, and register allocation. Exposure to run-time issues and optimization.
This course focuses on the design and implementation of modern operating systems. The course discusses threads, processes, virtual memory, schedulers, and the other fundamental primitives that an OS uses to represent active computations.
An exploration of the system call interface explains how applications interact with hardware and other programs which are concurrently executing. Case studies of popular file systems reveal how an OS makes IO efficient and robust in the midst of crashes and unexpected reboots. Students also learn how virtualization allows a physical machine to partition its resources across multiple virtual machines.
Class topics are reinforced through a series of intensive programming assignments which use a real operating system.
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We are in the big data era and data systems sit in the critical path of everything we do. We are going through major transformations in businesses, sciences, as well as everyday life - collecting and analyzing data changes everything and data systems provide the means to store and analyze a massive amount of data.
This course is a comprehensive introduction to modern data systems. We also study the history of data systems, traditional and seminal concepts and ideas such as the relational model, row-store database systems, optimization, indexing, concurrency control, recovery and SQL. In this way, we discuss both how and why data systems evolved over the years, as well as how these concepts apply today and how data systems might evolve in the future. We focus on understanding concepts and trends rather than specific techniques that will soon be outdated - as such the class relies largely on recent research material and on a semi-flipped class model with a lot of hands-on interaction in each class.
An introduction to key design principles and techniques for visualizing data. Covers design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Introduces programming of web-based interactive visualizations. This course covers the fundamentals of 3D computer graphics using a modern shader-based version of OpenGL.
Main topics include: geometric coordinate systems and transformations, keyframe animation and interpolation, camera simulation, triangle rasterization, material simulation, texture mapping, image sampling and color theory. The course also touches on ray tracing, geometric modeling and simulation-based animation.
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- Readings in computer vision: issues, problems, principles, and paradigms - Semantic Scholar?
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You will learn how to uncover needs that your customers cannot even articulate. You will also learn a range of design principles, effective creativity-related practices, and techniques for rapidly creating and evaluating product prototypes. You will also have several opportunities to formally communicate your design ideas to a variety of audiences. You will complete two large team-based design projects. Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory.
Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python. David Marr, a British mathematician and neuroscientist. Vision can be understood,. Marr proposed, outside the specifics of task or embodi-. There is "vision," according to Marr, not "visions.
His paradigm decomposed the study of vision into three. The algorithms for tracking, for instance, in a frog. Marr believed it was very important to separate the.