Machine Learning Foundations

What is Machine Learning:

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Common Usage of Machine Learning concept:

Database Mining for growth of automation: Typical applications include Web-click data for better UX( User eXperience), Medical records for better automation in healthcare, biological data and many more.

Writing software is the bottleneck, we don’t have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming scalable.

Traditional Programming: Data and program is run on the computer to produce the output.
Machine Learning: Data and output is run on the computer to create a program. This program can be used in traditional programming.

Applications of Machine Learning

Sample applications of machine learning:

Web search: ranking page based on what you are most likely to click on.
Computational biology: rational design drugs in the computer based on past experiments.
Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How to decide where to invest money.
E-commerce: Predicting customer churn. Whether or not a transaction is fraudulent.
Space exploration: space probes and radio astronomy.
Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
Information extraction: Ask questions over databases across the web.
Social networks: Data on relationships and preferences. Machine learning to extract value from data.
Debugging: Use in computer science problems like debugging. Labor intensive process. Could suggest where the bug could be.

What is your domain of interest and how could you use machine learning in that domain?

Key Elements of Machine Learning

There are tens of thousands of machine learning algorithms and hundreds of new algorithms are developed every year.

Every machine learning algorithm has three components:

Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.
Evaluation: the way to evaluate candidate programs (hypotheses). Examples include accuracy, prediction and recall, squared error, likelihood, posterior probability, cost, margin, entropy k-L divergence and others.
Optimization: the way candidate programs are generated known as the search process. For example combinatorial optimization, convex optimization, constrained optimization.

All machine learning algorithms are combinations of these three components. A framework for understanding all algorithms.

Types of Learning

There are four types of machine learning:

Supervised learning: (also called inductive learning) Training data includes desired outputs. This is spam this is not, learning is supervised.
Unsupervised learning: Training data does not include desired outputs. Example is clustering. It is hard to tell what is good learning and what is not.
Semi-supervised learning: Training data includes a few desired outputs.
Reinforcement learning: Rewards from a sequence of actions. AI types like it, it is the most ambitious type of learning.

Supervised learning is the most mature, the most studied and the type of learning used by most machine learning algorithms. Learning with supervision is much easier than learning without supervision.

Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x).

Classification: when the function being learned is discrete.
Regression: when the function being learned is continuous.
Probability Estimation: when the output of the function is a probability.