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Optimization for Machine Learning
Google Tech Talks
March, 25 2008
ABSTRACT
S.V.N. Vishwanathan - Research Scientist
Regularized risk minimization is at the heart of many machine learning algorithms. The underlying objective function to be minimized is convex, and often non-smooth. Classical optimization algorithms cannot handle this efficiently. In this talk we present two algorithms for dealing with convex non-smooth objective functions. First, we extend the well known BFGS quasi-Newton algorithm to handle non-smooth
functions. Second, we show how bundle methods can be applied in a machine learning context. We present both theoretical and experimental justification of our algorithms.
Speaker: S.V.N. Vishwanathan - Research Scientist - Zurich
S.V.N Vishwanathan is a principal researcher in the Statistical Machine Learning program, National ICT Australia with an adjunct appointment at the College of Engineering and Computer Science(CECS), Australian National University. I got my Ph.D in 2002 from the Department of Computer Science and Automation (CSA) at the Indian Institute of Science.
Length: 3344
Rating: 4.70 (3 ratings)
Tags: google techtalks techtalk engedu talk talks googletechtalks education
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Lecture 1 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, gives the introductory lecture for the course, Convex Optimization I (EE 364A).
Convex Optimization I concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.
Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=3940DD956CDF0622
EE 364A Course Website:
http://www.stanford.edu/class/ee364
Stanford University:
http://www.stanford.edu/
Stanford University Channel on YouTube:
http://www.youtube.com/stanford/
Length: 4833
Rating: 4.60 (15 ratings)
Tags: science electrical engineering technology convex optimization least squares constraint function portfolio circuit data fitting ellipsoid control signal processing
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Search Engine Optimization SEO Tutorial -- WebBizIdeas
Search Engine Optimization SEO Tutorial by WebBizIdeas is for beginners. We will cover SEO techniques that you can use TODAY that will increase your search engine rankings. We will go over the definition of search engine optimization, organic results, PPC, keyword research, competition research, competition analysis, on page & off page optimization, Meta tags, header tags, keyword density, URLs, site maps, xml site maps, google webmaster tools, link development, directory submission, local directories, online yellow pages, one-way links, two-way links, three-way links, article submission, rss feed distribution, blog submission, and online press release optimization.
Length: 543
Rating: 4.50 (11 ratings)
Tags: Search Engine Optimization SEO Tutotial WebBizIdeas internet marketing increase search engine rankings techniques
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