CIS 678 Artificial Intelligence problems deduction, reasoning knowledge representation planning learning natural language processing motion and manipulation social intelligence creativity

CIS 678 Learning why is it better than pre-programming a solution? where is it better than pre-programming a solution? what are its shortcomings?

CIS 678 Machine Learning Model a real life process by assuming a distribution and attempt to learn parameters

CIS 678 What do we need? Knowledge of statistics and probability Ability to process data Ability to apply principles of mathematics Statistics Math Computer Science ML

CIS 678 What can machine learning do? Classification –Predicting tax cheats –Quality control Association analysis –Encourage likely purchases –Identify unusual combinations

CIS 678 What can machine learning do? Regression –Prediction –Charting relationship Clustering (unsupervised) –Grouping similar objects –Describing groups

CIS 678 Classification discriminant prediction pattern recognition –optical character recognition –fingerprint, face and speech recognition compression outlier detection

CIS 678 Other concepts supervised versus unsupervised predictive versus descriptive reinforcement learning (game playing)

CIS 678 Probability sample space – the universe of possible outcomes events –a single outcome –example: E = the event that we roll a six probability –a number that is associated with the chances of a particular outcome –example: P(E) = 1/6