/  Computer Science Books /  Artificial Intelligence Books / 

Explorations in Artificial Intelligence and Machine Learning

Explorations in Artificial Intelligence and Machine Learning

Explorations in Artificial Intelligence and Machine Learning

This PDF covers the following topics related to Artificial Intelligence and Machine Learning, Introduction to Machine Learning,The Bayesian Approach to Machine Learning, A Revealing Introduction to Hidden Markov Models, Introduction to Reinforcement Learning, Deep Learning for Feature Representation, Neural Networks and Deep Learning, AICompleteness, The Problem Domain of Superintelligent Machines.

Author(s):

s178 Pages
Similar Books
Artificial Intelligence Foundations of Computational Agents

Artificial Intelligence Foundations of Computational Agents

This book contains below topics, What is artificial intelligence, Agent architectures and search, Reasoning with constraints, Propositions and inference, Planning, Supervised machine learning, Neural networks and deep learning, Reasoning and planning under uncertainty, Multiagent systems, Reinforcement learning.

s904 Pages
Introduction to Artificial Intelligence by Thomas P Trappenberg

Introduction to Artificial Intelligence by Thomas P Trappenberg

This note covers introduction and history, Search, Robotics and motion planning, Constraint satisfaction problem, Machine learning, Learning machines and the perceptron, Regression, classification and maximum likelihood, Support vector machines, Learning Theory, Generative models and Na and iuml,ve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.

s208 Pages
Explorations in Artificial Intelligence and Machine Learning

Explorations in Artificial Intelligence and Machine Learning

This PDF covers the following topics related to Artificial Intelligence and Machine Learning, Introduction to Machine Learning,The Bayesian Approach to Machine Learning, A Revealing Introduction to Hidden Markov Models, Introduction to Reinforcement Learning, Deep Learning for Feature Representation, Neural Networks and Deep Learning, AICompleteness, The Problem Domain of Superintelligent Machines.

s178 Pages
Lecture Notes On Artificial Intelligence By Dr Prashanta Kumar Patra

Lecture Notes On Artificial Intelligence By Dr Prashanta Kumar Patra

This book covers the following topics, AI Technique, Level of the Model,Problem Spaces, and Search, Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques, GenerateandTest, Hill Climbing, Bestfirst Search, Problem Reduction, Constraint Satisfaction, Meansends, Symbolic Reasoning Under Uncertainty, Game Playing, Learning, Rote Learning.

s128 Pages