Introduction to Machine Learning. Introduce yourself Why you choose this course? Any examples of machine learning you know?

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Presentation transcript:

Introduction to Machine Learning

Introduce yourself Why you choose this course? Any examples of machine learning you know?

Pre-requisites Discrete Mathematics Linear Algebra Statistics Calculus Optimization

Example Eigenvalue and Eigenvector Gradient Descent Convex Optimization Least Square Entropy Bayesian Theory

Couse Outline Webpage: Assignments (Quizs) Exam Presentation (Select papers by yourself) Projects (Team Work) – Estimate Missing Entries – Supervise Learning (Hiding Testing Label) – 3-5 Pages Project Report

Learning Learning is acquiring new, or modifying existing, knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information. The ability to learn is possessed by humans and animals.

Learning

What is Machine Learning? Arthur Samuel (1959). Machine Learning: Field of study that gives computers the ability to learn without being clearly programmed

Machine Learning Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Examples 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., handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.

Examples

Application Credit Approve (Credit Limit) Age Personal assets Salary …

Application Credit Card Fraud

Application

Netflix Price Netflix's Top 10 and other suggestions for you are based on your viewing habits

Netflix Price AgeEmployment

Application Seasonal Epidemic Influenza H1N1H3N2Influenza B2009 H1N1 40,000 deaths in US each year200,000 hospitalizations in US each year Billions of dollars lost

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Machine Learning  Unsupervised Learning  Technique of trying to find hidden structure in unlabeled data  Supervise Learning  Technique for creating a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs.