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Notes of Andrew Ng’s Machine Learning —— (0) Introduction

Welcome

Machine Learning

• Grew out of work in AI
• New capability for computers

Examples:

• 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 Progressing (NLP), Computer Vision.

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

• Understanding human learning (brain, real AI)

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), a more modern definition : Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some prformance measure P, if its performance on T, as measured by P, improves with experience E.

Example:

• Playing checkers.

  E = the experience of playing many games of checkers T = the task of playing checkers. P = the probability that the program will win the next game.

• Spam Filter

  E = Watching you label emails as spam or not spam T = Classifying emails as spam or not spam P = The number (or fraction) of emails correctly classified as spam / not spam.

Machine Learning algorithms

• Supervised learning
• Unsupervised learning

Others: Reinforcement learning, recommender system

Supervised Learning

In supervised learning, we are:

• given a data set
• given "right answers" (already know what our correct output should look like)
• having the idea that there is a relationship between the input and the output.

Categories of supervised learning problems

1. Regression: Predict continuous valued output

   Trying to map input variables to some continuous function.

   [To predict how much]

   E.g.

   • You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.
   • Given a picture of a person, we have to predict their age on the basis of the given picture

2. Classification: Discrete valued output (0/1 or 0/1/2/3...)

   Trying to map input variables into discrete categories.

   [To predict whether/which]

   E.g.

   • Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
   • You'd like software to examine individual customer accounts, and for each accout decide if it has been hacked/compromised.

Unsupervised Learning

In unsupervised learning we can:

• approach problems with little or no idea what our results should look like
• derive structure from data where we don't necessarily know the effect of the variables.
• 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.

E.g.

1. Clustering:

   • Google news looks for tens of thousands of news stories and automatically cluster them together. So, the news stories that are all about the same topic get displayed together.

   • 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.

2. 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).