David Alexander ​Forsyth is Fulton Watson Copp Chair in Computer Science at the University of Illinois at Urbana-Champaign, where he is a leading researcher in computer vision. Professor Forsyth has regularly served as a program or general chair for the top conferences in computer vision, and has just finished a second term as Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence.A Fellow of the ACM (2014) and IEEE (2009), Forsyth has also been recognized with the IEEE Computer Society’s Technical Achievement Award (2005), the Marr Prize, and a prize for best paper in cognitive computer vision (ECCV 2002).  Many of his former students are famous in their own right as academics or industry leaders.He is the co-author with Jean Ponce of Computer Vision: A Modern Approach (2002; 2011), published in four languages, and a leading textbook on the topic.Among a variety of odd hobbies, he is a compulsive diver, certified up to normoxic trimix level.

• 1 Notation and conventions.- 2 First Tools for Looking at Data.- 3 Looking at Relationships.- 4 Basic ideas in probability.- 5 Random Variables and Expectations.- 6 Useful Probability Distributions.- 7 Samples and Populations.- 8 The Significance of Evidence.- 9 Experiments.- 10 Inferring Probability Models from Data.- 11 Extracting Important Relationships in High Dimensions.- 12 Learning to Classify.- 13 Clustering: Models of High Dimensional Data.- 14 Regression.- 15 Markov Chains and Hidden Markov Models.- 16 Resources.

David Alexander ​Forsyth is Fulton Watson Copp Chair in Computer Science at the University of Illinois at Urbana-Champaign, where he is a leading researcher in computer vision. Professor Forsyth has regularly served as a program or general chair for the top conferences in computer vision, and has just finished a second term as Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence.A Fellow of the ACM (2014) and IEEE (2009), Forsyth has also been recognized with the IEEE Computer Society’s Technical Achievement Award (2005), the Marr Prize, and a prize for best paper in cognitive computer vision (ECCV 2002).  Many of his former students are famous in their own right as academics or industry leaders.He is the co-author with Jean Ponce of Computer Vision: A Modern Approach (2002; 2011), published in four languages, and a leading textbook on the topic.Among a variety of odd hobbies, he is a compulsive diver, certified up to normoxic trimix level.

• 1 Notation and conventions.- 2 First Tools for Looking at Data.- 3 Looking at Relationships.- 4 Basic ideas in probability.- 5 Random Variables and Expectations.- 6 Useful Probability Distributions.- 7 Samples and Populations.- 8 The Significance of Evidence.- 9 Experiments.- 10 Inferring Probability Models from Data.- 11 Extracting Important Relationships in High Dimensions.- 12 Learning to Classify.- 13 Clustering: Models of High Dimensional Data.- 14 Regression.- 15 Markov Chains and Hidden Markov Models.- 16 Resources.

David Alexander ​Forsyth is Fulton Watson Copp Chair in Computer Science at the University of Illinois at Urbana-Champaign, where he is a leading researcher in computer vision. Professor Forsyth has regularly served as a program or general chair for the top conferences in computer vision, and has just finished a second term as Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence.A Fellow of the ACM (2014) and IEEE (2009), Forsyth has also been recognized with the IEEE Computer Society’s Technical Achievement Award (2005), the Marr Prize, and a prize for best paper in cognitive computer vision (ECCV 2002).  Many of his former students are famous in their own right as academics or industry leaders.He is the co-author with Jean Ponce of Computer Vision: A Modern Approach (2002; 2011), published in four languages, and a leading textbook on the topic.Among a variety of odd hobbies, he is a compulsive diver, certified up to normoxic trimix level.

• 1 Notation and conventions.- 2 First Tools for Looking at Data.- 3 Looking at Relationships.- 4 Basic ideas in probability.- 5 Random Variables and Expectations.- 6 Useful Probability Distributions.- 7 Samples and Populations.- 8 The Significance of Evidence.- 9 Experiments.- 10 Inferring Probability Models from Data.- 11 Extracting Important Relationships in High Dimensions.- 12 Learning to Classify.- 13 Clustering: Models of High Dimensional Data.- 14 Regression.- 15 Markov Chains and Hidden Markov Models.- 16 Resources.