ML LEC 01 Intro to Supervised Learning
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This document is an introduction to a machine learning lecture that covers supervised learning and linear regression. The lecture aims to help students understand: - The supervised learning setting where the data consists of input-output pairs - Linear regression and how to apply linear regression models to make predictions - How to derive the least squares estimate for linear regression The document outlines that linear models can approximate many real processes and are often used as modules in larger systems. It also provides examples of regression problems and demonstrates linear prediction on sample data.
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ML LEC 01 Intro to Supervised Learning
- 1. Machine Learning LEC 01: Welcome Machine Learning L E C 0 1 TA1 Mohammad Akbari Instructor TAs 00-00 Time Welcome!
- 2. Machine Learning LEC 01: Welcome Outline • This lecture introduces us to the topic of supervised learning. Here the data consists of input-output pairs. Inputs are also often referred to as covariates, predictors and features; while outputs are known as variates, targets and labels. The goal of the lecture is for youto: - Understand the supervised learning setting. - Understand linear regression (aka least squares) - Understand how to apply linear regression models to make predictions. - Learn to derive the least squares estimate.
- 3. Machine Learning LEC 01: Welcome Linear supervised learning • Many real processes can be approximated with linear models. • Linear regression often appears as a module of larger systems. • Linear problems can be solved analytically. • Linear prediction provides an introduction to many of the core concepts of machine learning.
- 4. Machine Learning LEC 01: Welcome Regression: Output a scalar • Stock Market Forecast • Self-driving Car • Product Recommendation (eg: Amazon) ! = Dow Jones Industrial Average at tomorrow ! = ! = Control wheel angle User A, Product B Possibility to buy
- 5. Machine Learning LEC 01: Welcome
- 6. Machine Learning LEC 01: Welcome Energy demand prediction
- 7. Machine Learning LEC 01: Welcome |
- 8. Machine Learning LEC 01: Welcome |
- 9. Machine Learning LEC 01: Welcome Linear prediction
- 10. Machine Learning LEC 01: Welcome
- 11. Machine Learning LEC 01: Welcome Linear prediction on test data Likewise, for a point that we have never seen before, say x = [50 20], we generate the following prediction: y(x) = [1 50 20] = 1 + 0 + 10 = 11.
- 12. Machine Learning LEC 01: Welcome
- 13. Machine Learning LEC 01: Welcome
- 14. Machine Learning LEC 01: Welcome Optimization approach
- 15. Machine Learning LEC 01: Welcome Finding the solution by differentiation
- 16. Machine Learning LEC 01: Welcome Finding the solution by differentiation
- 17. Machine Learning LEC 01: Welcome Next lecture • In the next lecture, we learn to derive the linear regression estimates by maximum likelihood with multivariate Gaussian distributions. • Please go to the Wikipedia page for the multivariate Normal distribution beforehand or, even better, read Chapter 4 of Kevin Murphy’s book and attempt the first three questions of the exercise sheet.
- 18. Machine Learning LEC 01: Welcome Gradient Descent
- 19. Machine Learning LEC 01: Welcome Questions?