Introduction to Machine Learning - Basics

Basic lecture of Machine Learning Introduction, covering definition and types of Machine Learning

1. Introduction to Machine Learning

2. Motivation

3. Definition 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. Example: Handwritten Digit Recognition T: Classifying handwritten digits from images P: Percentage of digits classified correctly E: Dataset of digits given classification (MNIST)

4. A Little History

5. Types Supervised Learning - Labeled Data, Direct Feedback, Predict Outcome/Future Unsupervised Learning - Unlabeled Data, No Feedback, Find Hidden Structure in Data Reinforcement Learning - Decision Process, Reward System, Learn Series of Actions

6. Outline Supervised Learning Regression Models Classification Models Decision Trees Perceptron Neural Networks Unsupervised Learning Clustering