Posts List
  1. Preface…
  2. Application of Machine Learning
    1. Database Mining
    2. Applications can’t program by hand
    3. Self-customizing programs
  3. What is Machine Learning:
  4. Classifications of Machine Learning:
    1. Supervised Learning
      1. Two examples of supervised learning:
    2. Unsupervised Learning
      1. Two kinds of Unsupervised Learning
    3. Reinforcement Learning
  5. A Project

Machine Learning Learning Path

2017.05.23 Jialin Wu  热度 ℃

Preface…

I started learning machine learning during my second year in high school. This records my learning path of the technique. I started with gaining some basic understanding of machine learning. Here are some of the lecture notes I’ve kept during my study in Machine Learning in coursera.

Application of Machine Learning

Database Mining

  • Better and more customilized services
  • Medical Records
  • Biology
  • Engineering

Applications can’t program by hand

  • Autonomous helicopter
  • Hsandwriting recognition
  • NLP natural language processing / computer vision

Self-customizing programs

  • Amazon

What is Machine Learning:

  1. The field of study that gives computers the ability to learn without being explicitly programmed.
    Arthur Samuel
  2. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
    Tom Mitchell

Classifications of Machine Learning:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like. We already know that there is a relationship between the input and the output.

Two examples of supervised learning:

  • Regression:
    Predict continuous valued output

  • Classification:
    Discrete valued output(0 or 1)(or more)

Input data already have different labels.

Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structures from data where we don’t necessarily know the effect of the variables.

Two kinds of Unsupervised Learning

  • 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.
  • Unclustering
    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).

Input data have no labels in unsupervised learning.

Reinforcement Learning

Reinforcement Learning

A Project

With the basic understanding in mind, I decided to start with a project to boost my learning.