CSE599C1/STAT592, University of Washington Emily Fox February 5th, 2013 ©Emily Fox 2013 Case Study 2: Document Retrieval Gaussian Mixture Model ©Emily Fox 2013 2 ! Papers/Manuscripts. Students will gain hands-on experience through computing labs. Washington University in St. Louis' Master's of Science in Computer Science is directed toward students with a computer science background who are looking for a program and coursework that is software-focused. Course homepage: CSE 599Z, Spring 2010, University of Washington. Parameters: ! This course will classify different adaptive machine learning problems by characteristics such as the hypothesis space, the available actions, the measurement model, and the available side information. The schedule is tentative and can subject to change. CSE 599: Sketching Algorithms. Departments with a graduate data science option are found here. 6 pages. 6 pages. First class: Tuesday October 1, 2013. CSE 599: Sketching Algorithms. Assignments; Materials; Projects; Schedule; Schedule . Course Logistics. CSE 599W: Systems for ML. 3 Objective –To learn the main venues and developer resources for GPU computing –Where CUDA C fits in the big picture. Collecting such training sets can be expensive and time-consuming. A group is a collection of several projects. The standard approach to machine learning uses a training set of labeled examples to learn a prediction rule that will predict the labels of new examples. CSE 599G 599G . The Data Science option provides students an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. Join slack: https://uw-cse.slack.com dlsys channel We may use other time and locations for invited speakers. Artificial Intelligence, Machine Learning, Natural Language Processing. Graduate Courses CSE 556: Computational FabricationClass Page CSE 599-J1: Special Topics in Computational FabricationClass Page ME 599: Special Topics in Additive ManufacturingClass Page HCDE 598: Digital FabricationClass Page HCDE DRG: Fabricatable MachinesClass Page Undergraduate Courses ME 480: Introduction to Computer-Aided… Examples: Top-K arm identification, Learning a classifier with pool of unlabeled examples (e.g., google images), Techniques: Value, Q function learning by dynamic programming, Characteristics: stochastic state dependent actions, stochastic state transitions, regret-minimization, finite action space, Infinite Markov Decision Processes (MDPs), Algorithms: parameterized Q-learning, policy gradient, Characteristics: stochastic state dependent actions, stochastic state transitions, regret-minimization, (in)finite action space, Reading: [SuttonBarto Ch. Documents (3) Q&A; 599G Questions & Answers. Techniques: Active learning for binary classificaiton in streaming setting. CSE 599 D1: Advanced Topics in Natural Language Processing University of Washington - Spring 2018 Course Description. CSE 599 I Accelerated Computing - Programming GPUS Parallel Patterns: Prefix Sum (Scan) Accelerated Computing GPU Teaching Kit Parallel Reduction Module 9.1 – Parallel Computation Patterns (Reduction) 3 Objective –To learn the parallel reduction pattern –An important class of parallel computation –Work efficiency analysis –Resource efficiency analysis 3. Basic knowledge of machine learning (e.g CSE … You can manage your group member’s permissions and access to each project in the group. Sketching algorithms are powerful techniques to compress data in a way that lets you answer various queries. email: jlh@google.com. We will identify general adaptive strategies and cover common proof techniques. Scribing a lecture means summarizing the assigned papers and the lecture itself (with main theorems and proofs) as well as how this work fits into the context of the class so far and why it matters (e.g., linear bandits is a multi-armed bandit game with a given feature vector, a setting with applications to X, Y, Z). 2], Thompson Sampling [RussoEtAl]), Pure Exploration (e.g., [JamiesonNowak], [SimchowitzEtAl], [KarninKorenSomekh]), Regret minimization (e.g., EXP3 strategy of [SzepesvariLattimore, Linear experimental design (e.g., [RaskuttiMahoney], [Pukelsheim]), Pure-Exploration for linear bandits (e.g., [SoareLazaricMunos]), Non-linear active regression/MLE (e.g., [CastroWillettNowak], [ChaudhuriMykland]), Regret minimization for linear bandits (e.g., [AbbasiyadkoriEtAl]), Bayesian methods: Thompson Sampling, Information-directed sampling (e.g., [RussoEtAl], [RussoVanroy]), Contextual Bandits (e.g., [BubeckCesaBianchi Ch. In this course, we will review some of the highly influential papers which had a sustained impact on NLP research. CSE 591 Group Projects in Computer Science (1-3, max. 18 pages. Prerequisites: CSE 446 OR CSE 455 OR CSE 416. Our discussion will be guided by papers, monographs, and lecture notes that are available online. Rubric for research summary. For private or confidential questions email the instructor. If you organize your projects under a group, it works like a folder. CSE 601 Internship (1-2, max. Instructions will be sent out on how to submit your preferences over lectures -- the plan is to have each innermost bullet point be a single lecture. to refresh your session. 4 credits, CSE Core Course CSE Senior Elective . The course will be analysis heavy, with a focus on methods that work well in practice. Guanghao Ye (叶光昊) I’m a fourth-year BS/MS student at Paul G. Allen School of Computer Science & Engineering at the University of Washington, where I am very fortunate to be advised by Yin Tat Lee.. Adaptive submodularity optimization. 4]), Regret minimization (e.g., UCB strategy of [BubeckCesaBianchi Ch. if the paper uses a lemma from a different paper, you should understand that lemma and where it comes from). Assignment 1: Gesture based interaction System In this assignment, you will build a gesture based interaction system using the acoustic sensors in your device. This course explores a variety of modern techniques for learning to sample from an unknown probability distribution given examples. 1], [BubeckCesaBianchi Ch. Approximately half of the course will focus on techniques based on Markov Chain Monte Carlo techniques. Systems, On the Sample Complexity of the Linear Quadratic Regulator, Concentration Inequalities: A Nonasymptotic Theory of Independence, Theory of Classification: A Survey of Some Recent Advances, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Exponential weights for finite and continuous action spaces (e.g., [Bubeck Ch.

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