**Lecture Notes in Machine Learning**

by Zdravko Markov

**Publisher**: Central Connecticut State University 2003**Number of pages**: 65

**Description**:

Contents: Introduction; Concept learning; Languages for learning; Version space learning; Induction of Decision trees; Covering strategies; Searching the generalization / specialization graph; Inductive Logic Progrogramming; Unsupervised Learning; Explanation-based Learning.

Download or read it online for free here:

**Download link**

(340KB, PDF)

## Similar books

**Learning Deep Architectures for AI**

by

**Yoshua Bengio**-

**Now Publishers**

This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.

(

**6045**views)

**Elements of Causal Inference: Foundations and Learning Algorithms**

by

**J. Peters, D. Janzing, B. Schölkopf**-

**The MIT Press**

This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...

(

**4190**views)

**Statistical Foundations of Machine Learning**

by

**Gianluca Bontempi, Souhaib Ben Taieb**

This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. This manuscript aims to find a good balance between theory and practice.

(

**7410**views)

**Reinforcement Learning: An Introduction**

by

**Richard S. Sutton, Andrew G. Barto**-

**The MIT Press**

The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.

(

**24548**views)