Saturday, March 8, 2014

ML Class Notes: Lesson 1 - Introduction

I am taking the Machine Learning class at Coursera. These are my notes on the material presented by Professor Ng.

The first lesson introduces a number of concepts in machine learning. There is no code to show until the first algorithm is introduced in the next lesson.

Machine learning grew out of AI research. It is a field of study that gives computers the ability to learn algorithms and processes that can not be explicitly programmed. Computers could be programmed to do simple things, but doing more complicated things required the computer learn itself. A well posed learning program is said to learn some task if its performance improves with experience.

Machine Learning is used for a lot of things including data mining in business, biology and engineering; performing tasks that can't be programmed by hand like piloting helicopters or computer vision; self-customizing programs like product recommendations; and as a model to try to understand human learning.

Two of the more common categories of machine learning algorithms are supervised and unsupervised learning. Other categories include reinforcement learning and recommender systems, but they were not described in this lesson.

Supervised Learning

In supervised learning the computer taught to make predictions using a set of examples where the historical result is already known. One type of supervised learning tasks is regression where the predicted value is in a continuous range (the example given was predicting home prices). Other supervised learning algorithms perform classification where examples are sorted into two or more buckets (the examples given were of email, which can be spam or not spam; and tumor diagnosis which could be malignant or benign.)

Unsupervised Learning

In unsupervised learning, the computer must teach itself to perform a task because the "correct" answer is not known. A common supervised learning task is clustering. Clustering is used to group data points into different categories based on their similarity to each other. Professor Ng gave the the example of Google News, which groups related news articles, allowing you to select accounts of the same event from different news sources.

The unsupervised learning discussion ended with a demonstration of an algorithm that had been used to solve the "cocktail party problem", where two people were speaking at the same time in the same room, and were recorded by two microphones in different parts of the room. The clustering algorithm was used to determine which sound signals were from each speaker. In the initial recordings, both speakers could be heard on both microphones. In the sound files produced by the learning algorithm, each output has the sound from one speaker, with the other speaker almost entirely absent.

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