B - ML04 - Training Linear Models (1)

1. Training Linear Models

Prof. Iyad Jafar
https://sites.google.com/view/iyadjafar
iyad.jafar@ju.edu.jo

2. Reference

REFERENCE
Prof. Iyad Jafar
• Chapter 4: Training Models
• Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras and
TensorFlow, O’Reilly, 3rd Edition, 2023
• Material: https://github.com/ageron/handson-ml2
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3. Outline

Prof. Iyad Jafar
OUTLINE
• Introduction
• Linear Regression
• Gradient Descent Algorithm
• Polynomial Regression
• Learning Curves
• Regularized Linear Models
• Logistic Regression
• Softmax Regression
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4. Introduction

Prof. Iyad Jafar
INTRODUCTION

5. Introduction

INTRODUCTION
• So far we have treated machine learning models and their training
algorithms mostly like black boxes.
• Indeed, in many situations you don’t really need to know the
implementation details.
Prof. Iyad Jafar
• However, having a good understanding of how things work. Why?
• Pick the appropriate model, the right training algorithm to use, and a good set of
hyperparameters.
• Debug issues and perform error analysis more efficiently.
• The working of many ML algorithms is a foundation for understanding, building,
and training neural networks.
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6. Linear Regression

Prof. Iyad Jafar
LINEAR REGRESSION

7. Linear Regression

LINEAR REGRESSION
• A linear model makes a prediction by simply computing a weighted sum
of the input features, plus a constant called the bias/intercept.