IEEE Sir Padampat Singhania University

Dr K. R. Chowdhary explained about the following:
A machine learns with respect to a particular task T, performance matric P, and type of experience E, if the system improves its performance P, at task T, following experience E. Five tribes of machine learning algorithms: Symbolists, connectionists, Evolutionaries, Bayesians, Analogizers. Basic Learning categories: Supervised, Unsupervised and Reinforcement learning. Reinforcement Learning: Human beings always learn by interactions with the environment/teacher, which are cause and effect relations. Four components of RL: A policy, A value mapping, A reward function, A model of environment. Decision Tree Algorithm is to build a decision tree. Some Examples: Decision-tree for financing a client, Decision-tree for word sense disambiguation (WSD). The training database is accessed extensively while the tree is constructed. If the training database does not fit the memory, an efficient data-access method is needed to achieve the scalability. Hebb’s rule is cornerstone of connectionism: the fact that knowledge is stored in connections between neurons. The neurons that fire together wire together. Connectionist’s representations are distributed, each concept is represented by many neurons. Also, Details about: Learning to cure Cancer (Symbolists), Symbolists: Principle of Inverse Deduction, Classical Deduction V/s Inverse Deduction, Decision-tree to cut Computation (Symbolist).