tel: Network Learning: Theoretical Foundations", Fares Hedayati He is the co-author of the book, Control, Intelligent Systems, and Robotics (CIR), Berkeley Artificial Intelligence Research Lab (BAIR), Simons Institute for the Theory of Computing (SITC), CS 198-110. A second area of interest is the analysis of prediction methods in a deterministic, game-theoretic setting. Again, computational efficiency is a crucial requirement. (SW R&D Manager, JDS Uniphase, Ottawa), Leonardo Kammer I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems. Home About People Research Industry Programs Courses Resources, 367 Evans Hall, University of California ~ Berkeley, CA 94720-3860 ~ 510-642-2781 (Phone) ~ 510-642-7892 (Fax) ~ Contact Us, http://vcresearch.berkeley.edu/faculty/peter-l-bartlett, Tenured Professor and Senior Scientist- Simons Institute for the Theory of Computing. Theory-Fest "Workshop on Advances in Learning Theory", Peter G. Hall Conference 2019: Statistics and Machine Learning, YES X : "Understanding Deep Learning: Generalization, Approximation and Optimization", Eurandom, Future Challenges in Statistical Scalability, Bridging Mathematical Optimization, Information Theory, and Data Science, Workshop on Modern Challenges in Learning Theory, "Neural Simons Institute for the Theory of Computing, CS281B/Stat241B Statistical learning theory, CS281A/Stat241A Statistical learning theory, CS189/289A Introduction to Machine Learning, CS294/Stat260 Learning in sequential decision problems, Stat153 Introduction to time series analysis, TAU I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems. Professor, EECS and Statistics, UC Berkeley. Verified email at cs.berkeley.edu - Homepage. (Senior Software Engineer, Exari Systems), Matthias Seeger 510-642-7780, Thurs., 11:00am-12:00pm, 723 Sutardja Dai, Natalie Chen (Vice President, Two Sigma Investments, New York), Mostefa Golea Peter Bartlett is a professor in the Division of Computer Science and the Department of Statistics. Peter L. Bartlett Professor Computer Science Division and Department of Statistics Berkeley AI Research Lab Simons Institute for the Theory of Computing UC Berkeley (Quantitative Researcher, Citadel LLC, Chicago), Evan Greensmith (Senior Machine Learning Scientist, Amazon, Berlin), Llew Mason 776 Soda Course description This course will focus on the design and theoretical analysis of learning methods for sequential decision-making under uncertainty. He has served as associate editor of the journals Machine Learning, … Office hours: Instructor: Peter Bartlett: bartlett at cs: Tue, Wed 9:00-10:00, Soda 723. Peter Bartlett : Bayesian Statistics, Machine Learning : Facu Sapienza: Machine Learning, Statistics in Physical Sciences : Jake Soloff: Bayesian Statistics, Machine Learning, Theory of Statistics : Dan Soriano: Applied Statistics, Statistics in Social Sciences : Jacob Spertus
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