Lecture Notes for Machine Learning and Data Science Courses
This book covers the following topics: Introduction to Statistics, General Machine Learning Strategies, Linear Algebra, Regression Models, Causality, Assessing model goodness, Machine Learning Models, Different Types of Data, Machine Learning T echniques, Unsupervised Learning, Applications, Responsible Data Science
Author(s): Ott Toomet, Information School, University of Washington
437 Pages 
Digital Notes on Artificial Intelligence by Sri Indu College of Engineering and Technology
This note explains the following topics, Problem Solving by Search, Knowledge and Reasoning, Planning Classical Planning, Uncertain knowledge and Learning.
Author(s): Department of CSE, Sri Indu College of Engineering and Technology
140 Pages
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.
Author(s): Thomas P Trappenberg, Dalhousie University
208 Pages
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): Prof. Roberto V. Zicari
178 Pages
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.
Author(s): Dr. Prashanta Kumar Patra
128 Pages
