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people.eecs.berkeley.edu/~pabbeel
University of California at Berkeley Dept of Electrical Engineering & Computer Sciences CS 287: Advanced Robotics, Fall 2019 Fall 2015 offering (reasonably similar to current year's offering) Fall 2013 offering (reasonably similar to current year's offering) Fall 2012 offering (reasonably similar to current year's offering) Fall 2011 offering (fairly similar to current year's offering) Fall 2009 o
people.eecs.berkeley.edu/~jordan
Variational Bayesian Inference with Stochastic Search John Paisley1 jpaisley@berkeley.edu David M. Blei3 blei@cs.princeton.edu Michael I. Jordan1,2 jordan@eecs.berkeley.edu 1 Department of EECS, 2 Department of Statistics, UC Berkeley 3 Department of Computer Science, Princeton University Abstract Mean-field variational inference is a method for approximate Bayesian posterior inference. It approxi
Markov Decision Processes and Exact Solution Methods: Value Iteration Policy Iteration Linear Programming Pieter Abbeel UC Berkeley EECS [Drawing from Sutton and Barto, Reinforcement Learning: An Introduction, 1998] Markov Decision Process Assumption: agent gets to observe the state Markov Decision Process (S, A, T, R, H) Given n S: set of states n A: set of actions n T: S x A x S x {0,1,…,H} à
people.eecs.berkeley.edu/~matei
people.eecs.berkeley.edu/~brewer
people.eecs.berkeley.edu/~bodik
people.eecs.berkeley.edu/~russell
Q & A: The future of artificial intelligence What is artificial intelligence? It's the study of methods for making computers behave intelligently. Roughly speaking, a computer is intelligent to the extent that it does the right thing rather than the wrong thing. The right thing is whatever action is most likely to achieve the goal, or, in more technical terms, the action that maximizes expected ut
people.eecs.berkeley.edu/~kjamieson
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Kevin Jamieson on joint work with Lisha Li, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar (https://arxiv.org/abs/1603.06560) The hyperparamter optimization literature in recent years has been dominated by hyperparameter selection algorithms (e.g. Bayesian Optimization) that attempt to improve upon grid/random search
People Professor: Michael Jordan (jordan@cs.berkeley.edu) Office: 401 Evans, 2-8660; 731 Soda, 2-3806 Office hours: Tues, 3-4 (401 Evans); Thurs 1-2 (731 Soda) TA: Chao Chen (chenchao@stat.berkeley.edu) Office: 385 Evans Office hours: Mon, 1-2; Weds, 1-2 Course Description: This course will provide an introduction to advanced statistical and computational methods for the modeling of complex, multi
people.eecs.berkeley.edu/~christos
people.eecs.berkeley.edu/~pathak
Semantic Inpainting results on held-out images by Context Encoder. We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context
people.eecs.berkeley.edu/~keo
people.eecs.berkeley.edu/~lingqi
lingqi [at] berkeley [dot] edu Please visit http://www.cs.ucsb.edu/~lingqi/. This is my old website just kept here for archival purposes. Since July 1st, 2018, I've become an Assistant Professor at the Department of Computer Science at the University of California, Santa Barbara. I received my Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at the University of Cal
people.eecs.berkeley.edu/~necula
people.eecs.berkeley.edu/~svlevine
Associate Professor, UC Berkeley, EECS Address: Rm 8056, Berkeley Way West 2121 Berkeley Way Berkeley, CA 94704 Email: prospective students: please read this before contacting me. Follow @svlevine I am an Associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In my research, I focus on algorithms that can enable autonomous agents to acquire complex be
people.eecs.berkeley.edu/~jonlong
people.eecs.berkeley.edu/~kubitron
people.eecs.berkeley.edu/~sangjin
Posted on Dec 21, 2012 Scalable Event Multiplexing: epoll vs. kqueue I like Linux much more than BSD in general, but I do want the kqueue functionality of BSD in Linux. What is event multiplexing? Suppose that you have a simple web server, and there are currently two open connections (sockets). When the server receives a HTTP request from either connection, it should transmit a HTTP response to th
16 Who Says C is Simple? When I (George) started to write CIL I thought it was going to take two weeks. Exactly a year has passed since then and I am still fixing bugs in it. This gross underestimate was due to the fact that I thought parsing and making sense of C is simple. You probably think the same. What I did not expect was how many dark corners this language has, especially if you want to p
people.eecs.berkeley.edu/~bh
People keep asking me about the choice of programming language in 61A. Here is a rather longer explanation than I could give face to face. 1. The most important thing to understand: The choice of programming language is far from the most important thing in designing a course. The Berkeley party line is that you should be able to learn a programming language (after the first time) over a weekend. I
Why Structure and Interpretation of Computer Programs matters Brian Harvey University of California, Berkeley In 2011, to celebrate the 150th anniversary of MIT, the Boston Globe made a list of the most important innovations developed there. They asked me to explain the importance of SICP, and this is what I sent them: SICP was revolutionary in many different ways. Most importantly, it dramaticall
people.eecs.berkeley.edu/~wkahan
An Interview with the Old Man of Floating-Point Reminiscences elicited from William Kahan by Charles Severance 20 Feb. 1998 This interview underlies an abbreviated version to appear in the March 1998 issue of IEEE Computer. Introduction If you were a programmer of floating-point computations on different computers in the 1960's and 1970's, you had to cope with a wide variety of floating-point hard
File: Stnfrd50 version dated June 3, 2008 2:43 pm Prof. W. Kahan, University of California @ Berkeley Page 1/14 Why can I Debug some Numerical Programs that You Can’t ? Why should we care? What should we do? Presented in 23 min. on 30 March 2007 to the “Stanford 50” celebration of Stanford University’s 50th Anniversary of George Forsythe’s founding of Stanford’s Computer Science Dept., and also in
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