Relation and event extraction. Reference resolution. Machine translation. Supervised, semi-supervised, and active learning of linguistic models. Neural network models. There will be several pencil-and-paper exercises, several computer exercises, a final project, and a final exam.
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Prerequisites: Students should have solid programming skills in Java and should have successfully completed a graduate level course in natural language processing. The design of systems that can learn by reading. Ontology and knowledge base. Joint inference methods. Knowledge acquisition strategies. A good working knowledge of corpus-trained methods for these components is required using log-linear or deep models. Implementation will be in Java, so good programming skills in Java are needed, along with a commitment to meeting javadoc documentation standards. This is a capstone course.
A course in computer networks and large-scale distributed systems. Teaches the design and implementation techniques essential for engineering both robust networks and Internet-scale distributed systems.
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The goal is to guide students so they can initiate and critique research ideas in networks and distributed systems and implement and evaluate a working system that can handle a real-world workload. Topics include routing protocols, network congestion control, wireless networking, peer-to-peer systems, overlay networks and applications, distributed storage systems, and network security.
Prerequisites: 1. Large-scale distributed systems lie at the core of application domains such as cloud computing, internet of things, large multiplayer games, etc. These application domains make use of systems such as distributed databases e. In this class we will look at how to construct these distributed systems, in particular looking at why this is more complex than building applications running on a single machine, and present abstraction and design techniques for building distributed systems.
We will focus on a solving a variety of common problems in these systems including consensus, consistency, naming, fault tolerance, etc. This is a graduate-level course, but undergraduates are welcome!
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The course itself will consist of a series of lectures and will require reading research papers. This class satisfies the Ph. The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends using case studies and will need to iteratively propose and refine bold computing centric ideas for real problems.
Students will also initiate the process of translating their ideas to initial prototypes.
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Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.
Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works.
They will work in teams learning how to turn a great idea into a great company. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.
This class primarily concerns the design and analysis of Monte Carlo sam-pling techniques for the estimation of averages with respect to high dimen-sional probability distributions. Standard simulation tools such as importance sampling, sequential importance sampling, Gibbs and Metropolis-Hastings sampling, Langevin dynamics, and hybrid Monte Carlo will be introduced along with basic theoretical concepts regarding their convergence to equilibrium.
Particular attention will be paid to the major complicating issues like conditioning and rare events along with methods to address them e. This course covers theoretical and practical aspects of finite element methods for the numerical solution of partial differential equations.
The first part of the course will focus on theoretical foundations of the method calculus of variations, Poincare inequality, Cea's lemma, Nitsche trick, convergence estimates. The second part targets practical aspects of the method, illustrates how it can be implemented and used for solving partial differential equations in two and three dimensions.
Examples will include the Poisson equation, linear elasticity and, time permitting, the Stokes equations. Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning before class starts. Prior courses on machine learning are strongly recommended. This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference.
We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will be comprised of three units, alongside a major research project component. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference Laplace approximation, variational methods, MCMC , model selection and hypothesis testing, Occam's razor, non-parametric models. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization.
Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference. Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected. Many numerical or symbolic problems are very hard or even undecidable meaning that no computer algorithm can solve them exactly in reasonable time and memory space.
This course introduces the latest techniques in abstract interpretation, a powerful framework for automatically calculating approximate solutions of such difficult problems. The main emphasis will be on software verification and automatic analysis with a wide range of applications from compilers to social networks. This is a capstone course that connects students directly with real-world information technology problems. Each project lasts for the entire semester and is designed to involve the full software project life cycle.
Examples of such projects are development of software to solve a business problem, including specifying requirements, writing and testing prototype code, and writing a final report; and evaluation of commercial software to be purchased to address a business problem, including gathering requirements, designing an architecture to connect the new software with existing systems, and assessing the suitability of available software products.
Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies. Research Seminar. Thesis Research. Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph. Participation in a programming project or research project conducted outside the university in a governmental, commercial, or academic setting. Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed.
inartarcompters.tk MS students may repeat this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification. This course teaches key mathematical concepts using the new Python programming language.
The first part of the course teaches students how to use the basic features of Python: operations with numbers and strings, variables, Boolean logic, control structures, loops and functions. The second part of the course focuses on the phenomena of growth and decay: geometric progressions, compound interest, exponentials and logarithms. The third part of the course introduces three key mathematical concepts: trigonometry, counting problems and probability.
Students use Python to explore the mathematical concepts in labs and homework assignments.
No prior knowledge of programming is required. Prerequisites: No prior computing experience is assumed. Not intended for computer science majors. Addresses the impact of the digital computer on individuals, organizations, and modern society as a whole, and the social, political, and ethical issues involved in the computer industry. Topics change to reflect changes in technology and current events.
Features guest lecturers from various fields. Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA and pursue the major. An introduction to the fundamentals of computer programming, which is the foundation of computer science.