001. Machine learning 01_Welcome

Machine Learning
 - Grew out of work in AI
 - New capability for computers

Example:
 - Database mining
    Large datasets from growth of automation/web.
    E.g., Web click data, medical records, biology, engineering

 - Applications can't program by hand.
    E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing(NLP), Computer Vision.

 - Self-customizing programs
    E.g., Amazon, Netflix product recommendations

 - Understanding human learning(brain, real AI).


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.


Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?

a) Classifying emails as spam or not spam.              T

b) Watching you label emails as spam or not spam.         E

c) The number(or Fraction) of emails correctly classified as spam/not spam.    P

d) None of the above-this is not a machine learning problem.     



Machine learning algorithms:

-  Supervised learning
-  Unsupervised learning


Others: Reinforcement learning, recommender systems.

Also talk about: Practical advice for applying learning algorithms.