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They represent a wonderful family of programming environments useful both to computer scientists and to people in many other fields, such as computational biology and computational linguistics, in which data is naturally represented by strings.
This course provides an intensive training to develop skills in Unix command line tools and scripting that enable the accomplishment and automation of large and challenging computing tasks.The syllabus takes students from shell basics and piping, to regular-expression processing tools, to shell scripting and Python.SUNY University at Buffalo, NY This course is an introduction to operating system design and implementation.We study operating systems because they are examples of mature and elegant solutions to a difficult design problem: how to safely and efficiently share system resources and provide abstractions useful to applications.For the processor, memory, and disks, we discuss how the operating system allocates each resource and explore the design and implementation of related abstractions.
We also establish techniques for testing and improving system performance and introduce the idea of hardware virtualization.Programming assignments provide hands-on experience with implementing core operating system components in a realistic development environment.Course by ey Challen University of Washington CSEP552 is a graduate course on distributed systems.Distributed systems have become central to many aspects of how computers are used, from web applications to e-commerce to content distribution.This course will cover abstractions and implementation techniques for the construction of distributed systems, including client server computing, the web, cloud computing, peer-to-peer systems, and distributed storage systems.
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As a side effect, you practice teaching yourself new languages.Cornell University Types and Semantics is about designing and understand programming languages, whether they be domain specific or general purpose.The goal of this class is to provide a variety of tools for designing custom (programming) languages for whatever task is at hand.Part of that will be a variety of insights on how languages work along with experiences from working with academics and industry on creating new languages such as Ceylon and Kotlin.The class focuses on types and semantics and the interplay between them.
This means category theory and constructive type theory (e.Coq and richer variations) are ancillary topics of the class.The class also covers unconventional semantic domains such as classical linear type theory in order to both break students from convential thinking and to provide powerful targets capable of formalizing thinks like networking protocols, resource-sensitive computation, and concurrency constructs.The class project is to design and formalize a (programming) language for a purpose of the student's choosing, and assignments are designed to ensure students have had a chance to practice applying the techniques learned in class before culminating these skills in the class project.
UC Berkeley In this course, you will study advanced programming techniques including data structures, encapsulation, abstract data types, interfaces, and algorithms for sorting and searching, and you will get a taste of “software engineering”—the design and implementation of large programs.Full Lecture Materials Lecture of Spring 2016.This website contains full matrials including video links, labs, homeworks, projects.And it includes some other usefull resources for Java Documentation, Data Structure Resources, Git/GitHub and Java Development Resources.Resources Labs The link to labs and projects is included in the website.MIT This course is a first-year graduate course in algorithms.Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation.Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms.
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Most of these questions are (surprisingly?) difficult and far from being resolved.Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult).We will learn about these advances in this course.A theme will be combinatorial constructions with random-like properties, e., expander graphs and error-correcting codes.Some examples: Is finding a solution inherently more difficult than verifying it? Do more computational resources mean more computing power? Is it easier to find approximate solutions than exact ones? Are randomized algorithms more powerful than deterministic ones? Is it easier to solve problems in the average case than in the worst case? Are quantum computers more powerful than classical ones? Harvard University Extension College This course is all about understanding: understanding what's going on inside your computer when you flip on the switch, why tech support has you constantly rebooting your computer, how everything you do on the Internet can be watched by others, and how your computer can become infected with a worm just by being turned on.Designed for students who use computers and the Internet every day but don't fully understand how it all works, this course fills in the gaps.Through lectures on hardware, software, the Internet, multimedia, security, privacy, website development, programming, and more, this course "takes the hood off" of computers and the Internet so that students understand how it all works and why.Through discussions of current events, students are exposed also to the latest technologies.
Stanford University This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research.We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition.Students will also learn best practices to structure a model and manage research experiments.Stanford University Natural language processing (NLP) is one of the most important technologies of the information age.Understanding complex language utterances is also a crucial part of artificial intelligence.Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc.There are a large variety of underlying tasks and machine learning models powering NLP applications.Recently, deep learning approaches have obtained very high performance across many different NLP tasks.
These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering.In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models.The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.University of Texas Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets.The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++.
Topics covered include probability theory and Bayesian inference; univariate distributions; Central Limit Theorem; generation of random deviates; tail (p-value) tests; multiple hypothesis correction; empirical distributions; model fitting; error estimation; contingency tables; multivariate normal distributions; phylogenetic clustering; Gaussian mixture models; EM methods; maximum likelihood estimation; Markov Chain Monte Carlo; principal component analysis; dynamic programming; hidden Markov models; performance measures for classifiers; support vector machines; Wiener filtering; wavelets; multidimensional interpolation; information theory.Cornell University An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences.Tentative topic list: Dimensionality reduction, such as principal component analysis (PCA) and the singular value decomposition (SVD), canonical correlation analysis (CCA), independent component analysis (ICA), compressed sensing, random projection, the information bottleneck.(We expect to cover some, but probably not all, of these topics).Clustering, such as k-means, Gaussian mixture models, the expectation-maximization (EM) algorithm, link-based clustering.
(We do not expect to cover hierarchical or spectral clustering.
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Probabilistic-modeling topics such as graphical models, latent-variable models, inference (e., belief propagation), parameter learning.
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Carnegie Mellon University This course covers the theory and practical algorithms for machine learning from a variety of perspectives.It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning If you are working on your own machine, you will probably need to install Python. We will be using the standard Python software, available here. You should download and install version 2.5.4. TECHNICAL NOTE: Some modern operating systems, particularly OS X El Capitan, no longer allow Python 2.5.4 to be installed..It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning.The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor If you are working on your own machine, you will probably need to install Python. We will be using the standard Python software, available here. You should download and install version 2.5.4. TECHNICAL NOTE: Some modern operating systems, particularly OS X El Capitan, no longer allow Python 2.5.4 to be installed..The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor.Short programming assignments include hands-on experiments with various learning algorithms.
This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.Taught by one of the leading experts on Machine Learning - Tom Mitchell Stanford The course will cover a variety of contemporary network protocols and other systems with security properties.The course goal is to give students hands-on experience in using automated tools and related techniques to analyze and evaluate security mechanisms.To understand security properties and requirements, we will look at several network protocols and their properties, including secrecy, authentication, key establishment, and fairness.In parallel, the course will look at several models and tools used in security analysis and examine their advantages and limitations.
In addition to fully automated finite-state model checking techniques, we will also study other approaches, such as constraint solving, process algebras, protocol logics, probabilistic model checking, game theory, and executable models based on logic programming.UC Berkeley This class aims to provide a thorough grounding in network security suitable for those interested in conducting research in the area, as well as students more generally interested in either security or networking.We will also look at broader issues relating to Internet security for which networking plays a role.Topics include: denial-of-service; capabilities; network intrusion detection; worms; forensics; scanning; traffic analysis / inferring activity; architecture; protocol issues; legality and ethics; web attacks; anonymity; honeypots; botnets; spam; the underground economy; research pitfalls.The course is taught with an emphasis on seminal papers rather than bleeding-edge for a given topic.
Stanford The Web continues to grow in popularity as platform for retail transactions, financial services, and rapidly evolving forms of communication.It is becoming an increasingly attractive target for attackers who wish to compromise users' systems or steal data from other sites.Browser vendors must stay ahead of these attacks by providing features that support secure web applications.This course will study vulnerabilities in existing web browsers and the applications they render, as well as new technologies that enable web applications that were never before possible.The material will be largely based on current research problems, and students will be expected to criticize and improve existing defenses.
Your machine learning algorithms will classify handwritten digits and photographs.The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.UC Berkeley This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet.The course will cover a wide range of topics; see the lecture schedule for more details.While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic.
The course will also have several projects that involve programming (in Python).You should know programming, data structures, and software engineering.In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly.The TAs will spend very little time reviewing material that is not specific to networking.We assume that you either know the material covered in those courses, or are willing to learn the material as necessary.
We won't cover any of this material in lecture.UC Berkeley CS262a is the first semester of a year-long sequence on computer systems research, including operating systems, database systems, and Internet infrastructure systems.
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The goal of the course is to cover a broad array of research topics in computer systems, and to engage you in top-flight systems research.The first semester is devoted to basic thematic issues and underlying techniques in computer systems, while the second semester goes deeper into topics related to scalable, parallel and distributed systems.The class is based on a discussion of important research papers and a research project Should i get college homework transportation APA Editing double spaced 48 hours.
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Parts: Some Classics, Persistent Storage, Concurrency, Higher-Level Models, Virtual Machines, Cloud Computing, Parallel and Distributed Computing, Potpourri.Prerequisites: The historical prerequisite was to pass an entrance exam in class, which covered undergraduate operating systems material (similar to UCB's CS162) Writing term assignment with law help service trip essay master collection ielts essay for thesis assignment phtysics homework help help homework stypes soil. Buy a college legal studies homework cse rewriting 147. Science for kids: bacteria and germs. Soil horizon worksheet abitlikethis horizons images and photo .Prerequisites: The historical prerequisite was to pass an entrance exam in class, which covered undergraduate operating systems material (similar to UCB's CS162).However, if you have not already taken a decent undergrad OS class, you should talk with me before taking this class Writing term assignment with law help service trip essay master collection ielts essay for thesis assignment phtysics homework help help homework stypes soil. Buy a college legal studies homework cse rewriting 147. Science for kids: bacteria and germs. Soil horizon worksheet abitlikethis horizons images and photo .However, if you have not already taken a decent undergrad OS class, you should talk with me before taking this class.The exam had the benefit of "paging in" the undergrad material, which may have been its primary value (since the pass rate was high).
Cornell University Hardware that exploits quantum phenomena can dramatically alter the nature of computation.Though constructing a working quantum computer is a formidable technological challenge, there has been much recent experimental progress.In addition, the theory of quantum computation is of interest in itself, offering strikingly different perspectives on the nature of computation and information, as well as providing novel insights into the conceptual puzzles posed by the quantum theory.The course is intended both for physicists, unfamiliar with computational complexity theory or cryptography, and also for computer scientists and mathematicians, unfamiliar with quantum mechanics.
The prerequisites are familiarity (and comfort) with finite dimensional vector spaces over the complex numbers, some standard group theory, and ability to count in binary.
Cornell University How do we solve the large-scale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics co-processors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers.Topics include: Single-processor architecture, caches, and serial performance tuning Basics of parallel machine organization Distributed memory programming with MPI Shared memory programming with OpenMP Parallel patterns: data partitioning, synchronization, and load balancing Examples of parallel numerical algorithms Applications from science and engineering Cornell University CS5540 is a masters-level course that covers a wide range of clinical problems and their associated computational challenges.The practice of medicine is filled with digitally accessible information about patients, ranging from EKG readings to MRI images to electronic health records.This poses a huge opportunity for computer tools that make sense out of this data.Computation tools can be used to answer seemingly straightforward questions about a single patient's test results (“Does this patient have a normal heart rhythm?”), or to address vital questions about large populations (“Is there any clinical condition that affects the risks of Alzheimer”).
In CS5540 we will look at many of the most important sources of clinical data and discuss the basic computational techniques used for their analysis, ranging in sophistication from current clinical practice to state-of-the-art research projects.Cornell University This course will cover advanced topics in evolutionary algorithms and their application to open-ended computational design.The field of evolutionary computation tries to address large-scale optimization and planning problems through stochastic population-based methods.It draws inspiration from evolutionary processes in nature and in engineering, and also serves as abstract models for these phenomena.Evolutionary processes are generally weak methods that require little information about the problem domain and hence can be applied across a wide variety of applications.
They are especially useful for open-ended problem domains for which little formal knowledge exists and the number of parameters is undefined, such as for the general engineering design process.This course will provide insight to a variety of evolutionary computation paradigms, such as genetic algorithms, genetic programming, and evolutionary strategies, as well as governing dynamics of co-evolution, arms races and mediocre stable states.New methods involving symbiosis models and pattern recognition will also be presented.The material will be intertwined with discussions of representations and results for design problems in a variety of problem domains including software, electronics, and mechanics.Cornell University CS6630 is an introduction to physics-based rendering at the graduate level.
Starting from the fundamentals of light transport we will look at formulations of the Rendering Equation, and a series of Monte Carlo methods, from sequential sampling to multiple importance sampling to Markov Chains, for solving the equation to make pictures.We'll look at light reflection from surfaces and scattering in volumes, illumination from luminaries and environments, and diffusion models for translucent materials.We will build working implementations of many of the algorithms we study, and learn how to make sure they are actually working correctly.It's fun to watch integrals and probability distributions transform into photographs of a slightly too perfect synthetic world.Analytical Skills Because students need hands on experience alongside the theoretical lessons, it is important for them at some time during the study of electrical engineering, to enroll in internship and research programs.
Joining the programs help the students learn from mentors and get to know how things work in the real world of electrical engineering.The students get better at problem solving while also weight to their CV.The programs also come in Handy for networking and building successful electrical engineering careers.This intense study gets very overwhelming for many electrical engineering students.In between doing theories, going for internship and research programs, they are still required to do their electrical engineering assignments and pass with good grades.
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