《Foundation of Machine Learning [Part01]》.pdf

Foundations of Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Logistics Prerequisites: basics in linear algebra, probability, and analysis of algorithms. Workload: about 4 homework assignments + project (topic of your choice). Textbooks: no single textbook covering the material presented in this course, lecture slides available electronically. Mailing list: join as soon as possible. Mehryar Mohri - Foundations of Machine Learning page 2 Introduction to Machine Learning Machine Learning Definition: computational methods using experience to improve performance (typically to make accurate predictions). Experience: data-driven task (thus statistics, probability). Example: use height and weight to predict gender. Computer science: need to design efficient and accurate algorithms, analysis of complexity, theoretical guarantees. Mehryar Mohri - Foundations of Machine Learning page 4 Examples of Learning Tasks Optical character recognition. Text or document classification, spam detection. Morphological analysis, part-of-speech tagging, statistical parsing. Speech recognition, speech synthesis, speaker verification. Image recognition, face recognition. Mehryar Mohri - Foundations of Machine Learning page 5 Examples of Learning Tasks Fraud detection (credit card, telephone), network intrusion. Games (chess, backgammon). Unassisted control of a vehicle (robots, navigation). Medical diagnosis. Mehryar Mohri - Foundations of Machine Learning page 6 S