Introduction to Machine Learning

Welcome to an Introduction to Machine Learning!

About This Book¶

The materials in this “book” originated from course notes in the CS 171 Introduction to Machine Learning course at San José State University and I am slowly posting and updating them for a graduate-level MS 285 Machine Learning Application in Marine Science seminar course at the Moss Landing Marine Labs. Most of the examples are related to marine science, oceanography, climate science, or related fields.

These notes are intended to be an introduction to core topics in machine learning and a guide for students and researchers in both computer science and marine science. PyTorch is the primary computational tool used to illustrate models and workflows because it is flexibile and used widely in research. However, there are many examples that used regular computational tools like numpy and scipy and a few selected examples also use scikit-learn.

Table of Contents¶

• Introduction to Machine Learning
• Getting Started
  • Installing Python and Jupyter
  • A Brief Primer on Linear Algebra
• Basics
  • Overview
  • Linear Regression as a Machine Learning Model
  • Implementing Linear Regression as a Machine Learning Model
• Classifiers
  • Perceptrons
  • Logistic Regression
  • Classifiers in scikit-learn
  • k-Nearest Neighbors
  • Decision Trees
• Regression
  • Multiple Linear Regression
  • Random Forests
  • XGBoost and SHAP Analysis
• Neural Networks
  • Single-Layer Perceptrons from Scratch
  • Multi-Layer Perceptrons from Scratch
  • Neural Networks in PyTorch
• Convolutional Neural Networks
  • Image Classification
  • Image Segmentation
  • Object Detection with YOLO