"Machine Learning Fundamentals" Book
The companion repository for the book Machine Learning Fundamentals ©Hui Jiang 2021, published by Cambridge University Press.
Slides per chapter (Detailed contents is here)
- Ch 1: Introduction (slides)
- Ch 2: Mathematical Foundation (slides)
- Ch 3: Supervised Machine Learning (in a nutshell) (slides)
- Ch 4: Feature Extraction (slides)
- Ch 5: Statistical Learning Theory (slides)
- Ch 6: Linear Models (slides)
- Ch 7: Learning Discriminative Models in General (slides)
- Ch 8: Neural Networks (slides)
- Ch 9: Ensemble Learning (slides)
- Ch 10: Overview of Generative Models (slides)
- Ch 11: Unimodal Models (slides)
- Ch 12: Mixture Models (slides)
- Ch 13: Entangled Models (slides)
- Ch 14: Bayesian Learning (slides)
- Ch 15: Graphical Models (slides)
Lab Projects (using Jupyter Notebooks and/or Matlab)
NOTE: we are still working on these labs and each lab will be made public as soon as it is finalized.
- Lab 0: Preparation (available at Colab, ipynb, matlab)
- Lab 1: Data Visualization (available at Colab, ipynb, matlab)
- Lab 2: Linear Regression (available at Colab, ipynb, matlab)
- Lab 3: Logistic Regression (available at Colab, ipynb, matlab)
- Lab 4: Support Vector Machines (available at Colab, ipynb, matlab)
- Lab 5: Fully-Connected Neural Networks (available at Colab, ipynb, matlab)
- Lab 6: Convolutional Neural Networks (available at Colab, ipynb, matlab)
- Lab 7: Recurrent Neural Networks and Transformers
- Lab 8: Matrix Factorization
- Lab 9: Decision Trees, Random Forests and Boosted Trees
- Lab 10: Gaussian Classifiers and Gaussian Mixture Models
Citation (bibtex):
@book{Jiang-MLF-2021,
author = {Hui Jiang},
title = {Machine Learning Fundamentals},
publisher = {Cambridge University Press},
year = {2021}
}