Lecture 10: 8/6/1435 Machine Learning Lecturer/ Kawther Abas 363CS – Artificial Intelligence

What is Learning? Learning is any process by which a system improves performance from experience.”

3 main types of learning Supervised learning  learning with a teacher Unsupervised learning  Learning from pattern Reinforcement learning  Learning through experiences

Machine Learning Machine learning involves adaptive mechanisms that enable computers to learn from experience, learn by example and learn by analogy. Learning capabilities can improve the performance of an intelligent system over time.

Why is Machine Learning Important? Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships. Environments change over time.

Areas of Influence for Machine Learning Statistics Brain Models Adaptive Control Theory Psychology Artificial Intelligence Evolutionary Models

Designing a Learning System: An Example 1. Problem Description 2. Choosing the Training Experience 3. Choosing the Target Function 4. Choosing a Representation for the Target Function 5. Choosing a Function Approximation Algorithm 6. Final Design

When to learn 1.Human expertise does not exist. 2.Humans are unable to explain their expertise. 3.Solution changes in time. 4.Solution needs to be adapted to particular cases. Learning involves 1.Learning general models from data 2.Data is cheap and abundant. 3.Customer transactions to computer behaviour 4.Build a model that is a good and useful approximation to the data

Applications 1.Speech and hand-writing recognition 2.Autonomous robot control 3.Playing games 4.Clinical diagnosis 5.Web mining: search engines

Generic methods Learning from labelled data. Learning from unlabelled data. Learning from sequential data Associations Reinforcement Learning

Example 1: Hand-written digits Data representation: Greyscale images Task: Classification (0,1,2,3…..9) Problem features: Highly variable inputs from same class including some “weird” inputs, imperfect human classification, high cost associated with errors so “don’t know” may be useful.

Example 2: Speech recognition Data representation: features from spectral analysis of speech signals (two in this simple example). Task: Classification of vowel sounds in words of the form “h-?-d” Problem features: Highly variable data with same classification. Good feature selection is very important. Speech recognition is often broken into a number of smaller tasks like this.

Reinforcement Learning Learning a policy: A sequence of outputs No supervised output but delayed reward Credit assignment problem Game playing Robot in a maze Multiple agnts, partial observability

Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program

Sample Applications Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Debugging [Your favorite area]