Index
Lecture1
Note for Coursera Machine Learning made by Andrew Ng.
Introduction
What is Machine Learning
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 measured P, if its performance on T, as measured by P, imporves with experience E.
Machine Learning Algorithms
Machine Learning Algorithms
- Supervised learning
- Unsupervised learning
Others: Reinforcement learning, recommender systems etc.
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
There are two categorized supervised learning problems:
- Regression
- Classification
Regression - We are trying to predict results within a continuous output (Trying to map input variables to some continuous function).
Classification - We are trying to predict results in a discrete output. (Trying to map input variables into discrete categories).
Examples
Regression
Classification
Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Examples
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).