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Machine Learning Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data ➷ [Reading] ➹ Gender in Psychoanalytic Space By Muriel Dimen ➬ – Ralphslaurensoutlet.co.uk the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data



10 thoughts on “Machine Learning

  1. says:

    This is an introductory book on Machine Learning There is quite a lot of mathematics and statistics in the book, which I like A large number of methods and algorithms are introduced Neural Networks Bayesian Learning Genetic Algorithms Reinforcement LearningThe material covered is very interesting and clearly explained I find the presen


  2. says:

    Great intro to ML For someone who doesn t have a formal Comp Sci background, this took a lot out of me I found it helpful to stop after every chapter and listen to arecent lecture to tie loose ends Highly recommend reading this book in conjunction with professor Ng s ML intro course.


  3. says:

    This is a very compact, densely written volume It covers all the basics of machine learning perceptrons, support vector machines, neural networks, decision trees, Bayesian learning, etc Algorithms are explained, but from a very high level, so this isn t a good reference if you re looking for tutorials or implementation details However, it s quit


  4. says:

    This book is absolutely amazing I love so much is my favorite book.


  5. says:

    Great theoretically grounded intro to many ML topics.


  6. says:

    Really loved this book This was my introductory book into the how and why machine learning works I still come back to this book from time to time to serve as a reference point In my opinion Tom Mitchell serves up some good motivating examples for the algorithms and simply and clearly explains how they work.


  7. says:

    I learned a lot from this book The author assumes very little prior knowledge about math and statistics For that reason, he takes care to explain equations thoroughly from a rigorous and intuitive perspective.The book is old, and you ll see many references from 1980s and 1990s However, the content isn t about any specific technology it s about the foundational idea


  8. says:

    This book is a classic, but I can t stand it to me it embodies everything wrong with how machine learning is often taught ML people like to present the world from the point of view of optimizing a cost function for future examples, and see everything through this lens This worldview can be useful for graduate level research but it does not work for introductory teaching


  9. says:

    This books is old, but I thought it was a great introduction to Machine Learning I d never heard of Random Forrests before reading this book and this was very helpful, BUT of you already a lot about ML, consider something else This is an intuitive introduction for people who have basic math skills or first year grad students maybe even the motivated bachelor student.


  10. says:

    Probably the first book you want in academic setting when studying machine learning it s simple yet effective, and contains less mathematical mind twisters andconcepts of machine learning algorithms.


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