===== I. Introduction =====

==== 1.1 - Welcome ====
/* 1 - 1 - Welcome (7 min) */
  * 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 ====
/* 1 - 2 - What is Machine Learning- (7 min) */
  * Machine learning definition: 
    - Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
    - 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:
    - Supervised learning
    - Unsupervised learning
    - Also: Reinforcement learning, recommender systems. 

==== 1.3 - Supervised Learning ====
/* 1 - 3 - Supervised Learning (12 min) */

  * **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 ====
/* 1 - 4 - Unsupervised Learning (14 min) */

  * **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).