Machine Learning Fundamentals

This post provides complementary materials for my recent textbook Machine Learning Fundamentals by Hui Jiang, Cambridge University Press, 2021.

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)

• Lab 0: Preparation (available at Colab, ipynb)
• Lab 1: Data Visualization (available at Colab, ipynb)
• Lab 2: Linear Regression (available at Colab, ipynb)
• Lab 3: Logistic Regression (available at Colab, ipynb)
• Lab 4: Support Vector Machines (available at Colab, ipynb)
• Lab 5: Fully-Connected Neural Networks (available at Colab, ipynb)
• Lab 6: Convolutional Neural Networks (available at Colab, ipynb)
• Lab 7: Transformers (available at Colab, ipynb)
• Lab 8: Matrix Factorization (available at Colab, ipynb)
• 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} 
}