A list of reference reading for ECE5424: Advanced Machine Learning @VT

This post is going to summarize all the reference reading materials in the class ECE5424: Advanced Machine Learning by Professor Yue Joseph Wang¹²³ at Virginia Tech.

In the beginning of the post, textbooks that are recommended by Prof. Wang are listed, in addition, tens of papers are also provided which address different aspects of the topic of advanced machine learning. For each of the documents, you could place your cursor on the footnote to get the citation which embeds a link to the original webpage. The purpose for this article is to leave a note for the course that could be used during future research when Canvas would not be accessible after the semester is over.

Textbooks

There are a few textbooks Prof. Wang recommends.

“Pattern Classification“⁴

“Independent Component Analysis”⁵

“Neural Networks: A Comprehensive Foundation“⁶

“An Introduction to Statistical Signal Processing“⁷

“The elements of statistical learning: data mining, inference, and prediction“⁸

“An introduction to Signal Detection and Estimation“⁹

Reading

In terms of reading, actually there are lots of papers for students to read, the detailed list for reading is summarized as follows for your reference.

“Machine Learning for Science: State of the Art and Future Prospects“¹⁰

Clustering and Model Selection

“A Tutorial Introduction to the Minimum Description Length Principle“¹¹

“Model Selection and the Principle of Minimum Description Length“¹²

“Stability-Based Validation of Clustering Solutions“¹³

“K-means and Hierarchical Clustering“¹⁴

“Some Extensions of the K-Means Algorithms for Image Segmentation and Pattern Classification“¹⁵

Journals and Conferences

Several journals and conferences are mentioned in this course so that you could follow the newest trend from those sources.

1. https://ece.vt.edu/faculty/ywang.php[↩]
2. Google Scholar Profile[↩]
3. https://news.vt.edu/articles/2015/11/112315-engineering-wangprofessor.html[↩]
4. Duda, Richard O., and Peter E. Hart. 2006. Pattern Classification. John Wiley & Sons. 📁[↩]
5. Hyvärinen, Aapo, Juha Karhunen, and Erkki Oja. Independent Component Analysis. New York: John Wiley & Sons, 2001. 📁[↩]
6. Haykin, Simon. Neural Networks: A Comprehensive Foundation. 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2008. 📁[↩]
7. Gray, Robert M., and Lee D. Davisson. An introduction to statistical signal processing. Cambridge University Press, 2004. 📁[↩]
8. Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009. 📁[↩]
9. Poor, H. Vincent. An introduction to signal detection and estimation. Springer Science & Business Media, 1998. 📁[↩]
10. Mjolsness, Eric, and Dennis DeCoste. “Machine learning for science: state of the art and future prospects.” science 293.5537 (2001): 2051-2055. 📁[↩]
11. Grünwald, Peter. “Minimum description length tutorial.” Advances in minimum description length: Theory and applications 5 (2005): 1-80. 📁[↩]
12. Hansen, Mark H., and Bin Yu. “Model selection and the principle of minimum description length.” Journal of the American Statistical Association 96.454 (2001): 746-774. 📁[↩]
13. Lange, Tilman, et al. “Stability-based validation of clustering solutions.” Neural computation 16.6 (2004): 1299-1323. 📁[↩]
14. Moore, Andrew. “K-means and Hierarchical Clustering.” (2001). 📁[↩]
15. Marroquin, Jose L., and Federico Girosi. “Some extensions of the k-means algorithm for image segmentation and pattern classification.” (1993). 📁[↩]