public:courses:machine_learning:machine_learning:introduction

Table of Contents

I. Introduction

I. Introduction

1.1 - Welcome

Machine Learning is used everywhere: automation, post mail, brain study, Natural language processing (NLP), computer vision, etc… It is one of the top fields for IT skills.

1.2 - What is Machine Learning

Machine learning definition:
1. Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
2. Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Main types of algorithms:
1. Supervised learning
2. Unsupervised learning
3. Also: Reinforcement learning, recommender systems.

1.3 - Supervised Learning

Regression : trying to generate a continuous output value from the input sample.
Classification: trying to assign a clearly define discrete value to the inpu sample.
For classification problems we may have more than one feature to classify on.
We can even deal with an infinite number of features with the Support Vector machine algorithm.

1.4 - Unsupervised Learning

Clustering algorithm: used to separate the dataset samples into different clusters.
With unsupervised learning, the idea is to automatically find structure into the dataset.
Can be used to separate voices from two microphones for instance. Or separte voice from music (still with two microphones).