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Introduction To Machine Learning - Ethem Alpaydin Pdf


This website is inspired by the datasciencemasters/go and open-source-cs-degree Github pages. This one is specifically for machine learning and features textbooks, textbook-length lecture notes, and similar materials found with a simple google search. This repository is meant as a general guide and resource for a free education.




Introduction To Machine Learning - Ethem Alpaydin Pdf



  • CS 4780 - Fall 2009Cornell UniversityDepartment of Computer Science Time and PlaceFirst lecture: August 27, 2009Last lecture: December 3, 2009Tuesday, 1:25pm - 2:40pm in Phillips 203

  • Thursday, 1:25pm - 2:40pm in Phillips 203

NOTE: CS4780 is only offered in Fall 2009, not in Spring 2010. First Prelim Exam: 10/15Second Prelim Exam: 11/24Review Session I: Wednesday 10/14, 10:00am - 11:00am, in Upson 315 Review Session II: Sunday 11/22, 5:00pm - 6:00pm, in Upson 315InstructorThorsten Joachims,tj@cs.cornell.edu, 4153 Upson Hall.Mailing List and Newsgroup[cs4780-l@cornell.edu] We'd like you to contact us by using this mailing list. The list is set to mail all the TA's and profs -- you will get the best response time by using this facility, and all the TA's will know the question you asked and the answers you receive. Teaching AssistantsMark Verheggen,mark@cs.cornell.edu, Upson 4161.Office HoursMonday, 1:00 pm - 2:00 pmMark VerheggenUpson 328BThursday, 3:00 pm - 4:00 pmThorsten Joachims4153 UpsonThursday, 12:15 pm - 1:15 pmMark VerheggenUpson 328BFriday, 2:30 pm - 3:30 pmRick DucottUpson 328BSyllabusMachine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, or to detect fraudulent credit card transactions. This course will introduce the fundamental set of techniques and algorithms that constitute machine learning as of today, ranging from classification methods like decision trees and support vector machines, over structured models like hidden Markov models and context-free grammars, to unsupervised learning and clustering. The course will not only discuss individual algorithms and methods, but also tie principles and approaches together from a theoretical perspective. In particular, the course will cover the following topics:


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