Download A Brief Introduction to Machine Learning for Engineers pdf, This monograph aims at providing an introduction to key ideas, algorithms, and theoretical leads to machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental ideas and algorithms by building on initial principles, whereas also exposing the reader to a lot of advanced topics with in depth pointers to the literature, among a unified notation and mathematical framework. Download the pdf from below to explore all topics and start learning.


I Basics
1 Introduction
2 A Gentle Introduction through Linear Regression
3 Probabilistic Models for Learning

II Supervised Learning
4 Classification
5 Statistical Learning Theory∗

III Unsupervised Learning
6 Unsupervised Learning

IV Advanced Modelling and Inference
7 Probabilistic Graphical Models
8 Approximate Inference and Learning

V Conclusions
9 Concluding Remarks

A Appendix A: Information Measures
B Appendix B: KL Divergence and Exponential Family