Computer Programming in Java

COLUMBIA UNIVERSITY HIGH SCHOOL SCIENCE HONORS PROGRAM 2007Apr 14 Sat Week 10 1. Machine Learning Intro Wikipedia: As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to "learn". Most of machine learning works on extracting rules and patterns out of massive data sets. Some parts of machine learning are closely related to data mining and statistics. Machine learning research is focused on the computational properties of the statistical methods, such as their computational complexity. Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing, and robot locomotion. 2. Basic Probability First we will cover some basic probability. Conditional probability: P(A | B) = P(A and B) / P(B) Definition of independence: P(A and B) = P(A) * P(B) Bayes Rule: P(A | B) = (P(B | A) * P(A)) / P(B) 3. Classification Classification is a major task within machine learning. The general problem is as follows: we are given a dataset where each example within that dataset belongs to a certain class. For example, we could be given a set of programs that are either viruses or non-viruses (i.e. they are benign files). We wish to train our system on this dataset so that given a new, unknown example, we can with high accuracy predict the correct class.