ml-lecture01

ml-lecture01 - Machine Learning (COMP-652) Instructor:...

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Unformatted text preview: Machine Learning (COMP-652) Instructor: Doina Precup Email: dprecup@cs.mcgill.ca Teaching assistant: Cosmin Paduraru Email: cosmin@cs.mcgill.ca Class web page: http://www.cs.mcgill.ca/ dprecup/courses/ml.html September 5, 2007 1 COMP-652 Lecture 1 Outline Administrative issues What is machine learning? Types of machine learning Supervised learning Linear regression September 5, 2007 2 COMP-652 Lecture 1 Administrative issues Class materials: Six textbooks that you can use for reference (none required): * Bishop * Mitchell * Duda, Hart and Stork * Alpaydin * Hastie, Tibshirani and Friedman * Sutton and Barto Additional readings: distributed in class and/or posted on the web page Class notes: posted on the web page September 5, 2007 3 COMP-652 Lecture 1 Administrative issues Prerequisites: Basic knowledge of a programming language Some knowledge of probabilities and statistics Some AI background is recommended but not required Matlab will be used in assignments but prior knowledge is not required September 5, 2007 4 COMP-652 Lecture 1 Evaluation Seven homework assignments, top six count (60%) Midterm examination (15%) Project (25%) Participation to class discussions (up to 2% extra credit) September 5, 2007 5 COMP-652 Lecture 1 What is learning? What is machine learning? September 5, 2007 6 COMP-652 Lecture 1 What is learning? H. Simon: Any process by which a system improves its performance M. Minsky: Learning is making useful changes in our minds R. Michalsky: Learning is constructing or modifying representations of what is being experienced L. Valiant: Learning is the process of knowledge acquisition in the absence of explicit programming September 5, 2007 7 COMP-652 Lecture 1 Why study machine learning? Easier to build a learning system than to hand-code a working program! E.g.: Robot that learns a map of the environment by wandering around it Programs that learn to play games by playing against themselves Improving on existing programs, e.g. Instruction scheduling and register allocation in compilers Combinatorial optimization problems Discover knowledge and patterns in highly dimensional, complex data September 5, 2007 8 COMP-652 Lecture 1 Why study machine learning? Solving tasks that require a system to be adaptive, e.g. Speech and handwriting recognition Intelligent user interfaces Understanding animal and human learning How do we learn language? How do we recognize faces? Creating real AI! If an expert systembrilliantly designed, engineered and implementedcannot learn not to repeat its mistakes, it is not as intelligent as a worm or a sea anemone or a kitten. (Oliver Selfridge)....
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This note was uploaded on 09/04/2008 for the course COMP 652 taught by Professor Preicup during the Fall '07 term at McGill.

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ml-lecture01 - Machine Learning (COMP-652) Instructor:...

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