PPT-More Large-Scale Machine Learning

Perceptrons SupportVector Machines Jeffrey D Ullman Stanford University The Perceptron Given a set of training points x y where x is a realvalued vector of d dimensions and y is a binary decision 1 or 1. By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Pritam. . Sukumar. & Daphne Tsatsoulis. CS 546: Machine Learning for Natural Language Processing. 1. What is Optimization?. Find the minimum or maximum of an objective function given a set of constraints:. Jimmy Lin and Alek . Kolcz. Twitter, Inc.. Presented by: Yishuang Geng and Kexin Liu. 2. Outline. •Is twitter big data? . •How . can machine learning help twitter?. •Existing challenges?. •Existing literature of large-scale learning. Machine Learning. Large scale machine learning. Machine learning and data. Classify between confusable words.. E.g., {to, two, too}, {then, than}.. For breakfast I ate _____ eggs.. “It’s not who has the best algorithm that wins. . via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. Alon. Halevy, Peter . Norvig. and Fernando Pereira. Google. 2011. 10. 24. Eun. -Sol Kim. The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve.. CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Course Project. Write-ups due June 1. st. Project presentations . 12 presentations, 10 mins each, 15 min slack. Tarek Elgamal. 2. , . Shangyu. Luo. 3. , . Matthias Boehm. 1. , Alexandre V. Evfimievski. 1. , . Shirish. Tatikonda. 4. , . Berthold Reinwald. 1. , . Prithviraj. Sen. 1. 1. IBM Research – . Page 46 L istening to the voice of customers plays a prominent role in a customer-centric business strategy. But with the business environment’s increased complexity and dynamism for a customer- Dangers and Opportunities. Davide Faranda . CNRS – LSCE. M. Vrac, P. . Yiou. , F.M.E. Pons, A. . Hamid, G. . . Carella. , . C.G. . Ngoungue. . Langue, S. . Thao, V. . Gautard. IN2P3-IRFU. Context. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly. Sylvia Unwin. Faculty, Program Chair. Assistant Dean, iBIT. Machine Learning. Attended TDWI in Oct 2017. Focus on Machine Learning, Data Science, Python, AI. Started with a catchy opening speech – “BS-Free AI For Business”.