Best Textbooks to Accompany Andrew Ng's CS229 Machine Learning Course

Andrew Ng's CS229 course is a highly regarded introduction to machine learning, but enhancing your understanding with supplementary materials can greatly enrich your learning experience. Here are recommendations for several textbooks that complement the course, covering a range of topics from theoretical foundations to practical applications.

Comprehensive Introduction to Machine Learning

To get a strong foundation in the probabilistic approach to machine learning, An Introduction to Statistical Learning is an excellent choice. Unlike some other texts that use Octave, this book provides a detailed overview of machine learning concepts using R, offering both theoretical insights and practical examples. This supplemented by clear explanations of the mathematical underpinnings, making it a valuable companion to the CS229 course.

Deep Learning Primer

For those interested in the cutting-edge aspects of neural networks, Deep Learning, authored by the prominent figure of Geoffrey Hinton, Yoshua Bengio, and Yann LeCun, is a must. This book is widely recognized as a solid primer on deep learning and is freely available online. While the entire text covers a broad spectrum of neural network topics, the first half is particularly relevant to the CS229 course, as it focuses on essential concepts and techniques in the field of machine learning. To deepen your understanding further, consider exploring other material from Ng's newer course, which expands on neural networks significantly.

Research-Level Texts for Machine Learning

For serious researchers delving into advanced topics, two highly recommended books are The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, and Probabilistic Graphical Models by Daphne Koller and Nir Friedman. The Elements of Statistical Learning is a seminal work that provides a comprehensive overview of advanced statistical learning techniques, making it a bible for serious students of the field. It is particularly useful for understanding the probabilistic approach to machine learning, which aligns well with the course material.

Another detailed introduction, albeit with slightly less mathematical rigor, is Machine Learning - A Probabilistic Approach by Kevin P. Murphy. This book offers a thorough treatment of probabilistic models, including detailed explanations of machine learning algorithms and their probabilistic formulations. It serves as an excellent resource for those who need a deeper understanding of the theoretical underpinnings and advanced applications of machine learning techniques.

Introduction to Machine Learning by Ethem Alpaydin is a beginner-friendly introduction to machine learning concepts without delving too deeply into mathematical details. It is ideal for those who want to grasp the basic principles and applications of machine learning before tackling more advanced topics. Although it may not cover all the nuances of advanced machine learning, it provides a solid foundation for further study.

In addition to these books, Computational Intelligence - An Introduction by Andries P. Engelbrecht offers a simpler perspective on machine learning, making it accessible for learners who prefer a less math-intensive approach. However, it omits some advanced topics and is more suited for beginners or those looking for a lighter introduction to the field.

Resources for Further Learning

If you're interested in exploring more online resources, OpenCourser is a site dedicated to helping learners find high-quality online courses. While the CS229 course material is sufficient for simple real-life applications, these additional resources can help bridge the gap between theoretical knowledge and practical application.

In conclusion, the best textbooks to complement Andrew Ng’s CS229 course vary based on your level of experience and interest in the field. Whether you are seeking a foundational understanding or delving into advanced concepts, these resources can significantly enhance your learning journey in machine learning.

References:

An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Machine Learning - A Probabilistic Approach, Kevin P. Murphy. Introduction to Machine Learning, Ethem Alpaydin. Computational Intelligence - An Introduction, Andries P. Engelbrecht.