Machine Learning Fundamentals

©Hui Jiang 2021, Cambridge University Press

This project is about my recent textbook (Jiang, 2021):

Machine Learning Fundamentals ©Hui Jiang 2021, published by Cambridge University Press.

GitHub Site: the companion repository for supplementary materials
Book Blog
Supplementary Lab Projects:
- Lab 0: Preparation
- Lab 1: Data Visualization
- Lab 2: Linear Regression
- Lab 3: Logistic Regression
- Lab 4: Support Vector Machines
- Lab 5: Fully-Connected Neural Networks
- Lab 6: Convolutional Neural Networks
- Lab 7: Transformers
- Lab 8: Matrix Factorization
- Lab 9: Decision Trees, Random Forests and Boosted Trees
- Lab 10: Gaussian Classifiers and Gaussian Mixture Models

References

2021

MLF-Book
Machine Learning Fundamentals

Hui Jiang

2021

Abs DOI Bib HTML Slides

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
@book{Jiang-MLF-2021, author = {Jiang, Hui}, title = {Machine Learning Fundamentals}, publisher = {Cambridge University Press}, year = {2021}, doi = {https://doi.org/10.1017/9781108938051}, url = {https://books.google.ca/books?id=jSFGEAAAQBAJ}, }