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CS 229: Machine Learning (Course handouts)

CS 229: Machine Learning (Course handouts)
Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm. (optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Supplemental notes 1 (pdf) Binary classification with +/-1 labels. Supplemental notes 2 (pdf) Boosting algorithms and weak learning.

Big Data and Social Analytics certificate course | MIT Earn an MIT certificate in the fundamental theory and analysis of big data to better understand and predict human networks and behaviours in social structures. Conduct preliminary analysis and draw hypotheses about that data Understand exactly what kind of data you are dealing with in your role Design interventions using that analysis that are intended to change behavior Massachusetts Institute of Technology | School of Architecture + Planning Privacy Policy | Call us: +1 224 249 3522 Understanding these human-machine systems is what's going to make our future social systems stable and safe. Founding faculty director of MIT Connection Science; Course Instructor Receive detailed course module information, pricing, course schedules, and more. Yves-Alexandre de Montjoye SUITABLE FOR: This course is suitable for technically-minded graduates and working professionals in any role, across any industry, who are looking to unlock big data potential in their organisation.

Introduction to Machine Learning Draft of Incomplete Notes by Nils J. nilsson@cs.stanford.edu Description (as of ): From this page you can download a draft of notes I used for a Stanford course on Machine Learning. The notes survey many of the important topics in machine learning circa the late 1990s. There have been many important developments in machine learning since these notes were written. Download the notes: Introduction to Machine Learning (2.1 MB) Although this draft says that these notes were planned to be a textbook, they will remain just notes. Nils J. nilsson@cs.stanford.edu Copyright © 2014 Nils J.

Andrew Ng's Home page Andrew Ng is a co-founder of Coursera and the director of the Stanford AI Lab. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera. more > Ng’s Stanford research group focuses on deep learning, which builds very large neural networks to learn from labeled and unlabeled data. more > Video Lectures | Mathematics for Computer Science | Electrical Engineering and Computer Science | MIT OpenCourseWare Machine Learning - complete course notes The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming. All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. What are these notes? Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! The notes were written in Evernote, and then exported to HTML automatically. How can you help!? If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Content

Bit Twiddling Hack By Sean Eron Anderson seander@cs.stanford.edu Individually, the code snippets here are in the public domain (unless otherwise noted) — feel free to use them however you please. The aggregate collection and descriptions are © 1997-2005 Sean Eron Anderson. The code and descriptions are distributed in the hope that they will be useful, but WITHOUT ANY WARRANTY and without even the implied warranty of merchantability or fitness for a particular purpose. Contents About the operation counting methodology When totaling the number of operations for algorithms here, any C operator is counted as one operation. Compute the sign of an integer The last expression above evaluates to sign = v >> 31 for 32-bit integers. Alternatively, if you prefer the result be either -1 or +1, then use: sign = +1 | (v >> (sizeof(int) * CHAR_BIT - 1)); // if v < 0 then -1, else +1 On the other hand, if you prefer the result be either -1, 0, or +1, then use: sign = (v ! Detect if two integers have opposite signs f = v && !

Game Theory - Stanford University, The University of British Columbia Popularized by movies such as "A Beautiful Mind," game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games' in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more.

CS345: Data Mining Data Mining Winter 2010 Course information: Instructors: Jure LeskovecOffice Hours: Wednesdays 9-10am, Gates 418 Anand RajaramanOffice Hours: Tuesday/Thursday 5:30-6:30pm (after the class in the same room) Room: Tuesday, Thursday 4:15PM - 5:30PM in 200-203 (History Corner). Teaching assistants: Abhishek Gupta (abhig@cs.stanford.edu). Roshan Sumbaly (rsumbaly@cs.stanford.edu). Staff mailing list: You can reach us at cs345a-win0910-staff@lists.stanford.edu Prerequisites: CS145 or equivalent. Materials: Readings have been derived from the book Mining of Massive Datasets. Students will use the Gradiance automated homework system for which a fee will be charged. You can see earlier versions of the notes and slides covering 2008/09 CS345a Data Mining. Requirements: There will be periodic homeworks (some on-line, using the Gradiance system), a final exam, and a project on web-mining. Projects: Course outline See Handouts for a list of topics and reading materials. Announcements: Important Dates

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