Machine Learning

Machine Learning#

Mahmood Amintoosi, Spring 2025

Computer Science Dept, Ferdowsi University of Mashhad

Note

These lectures were built using the new Sphinx-based Jupyter Book 2.0 tool set, as part of the ExecutableBookProject. They are intended mainly as a demonstration of these tools. Instructions for how to build them from source can be found in the Jupyter Book documentation.

About Machine Learning#

In this exciting journey, we’ll delve into the fascinating world of Machine Learning with Python, where machines learn, adapt, and make decisions based on data. Topics that are covered are Linear Models, Kernel Trick, Model Selection, Ensemble Learning, Data Preprocessing and Bayesian Learning. Neural Networks, Gradient Descent and related subjects are disccussed in Neural Networks Course.

Prerequisites#

  • Basic Knowledge: You should have a solid understanding of artificial intelligence concepts.

  • Mathematics: Advanced topics may require strong mathematical foundations.

  • Current students of MDS at FUM University are acquainted with the concepts covered in the FDS Course

Questions?#

I will be having office hours for this course on Monday (10:00 AM–11:30 AM). If this is not convenient, email me at m.amintoosi@um.ac.ir, talk to me after class or schedule an appointment via Calendly.

Our Slack workspace#

Come and join our Slack group of Computer Science Dept, Ferdowsi University of Mashhad, to engage in course discussions.

I should mention that the original material of this course was from Open Machine Learning Course, by Joaquin Vanschoren and others. I have forked his repository and modified it to suit my own needs and preferences. I would like to thank him for his great work and generosity.

References#

[Asa16]

Alale Asaran. Super resolution via sparserepresentation. Master's thesis, Hakim Sabzevari University, Faculty of Mathematics and Computer Science, Winter 2016. فراتفکیک پذیری با نمایش تنک. URL: http://hcloud.hsu.ac.ir/index.php/s/W9ImIzeV6C1mqZo.

[Bak18]

Mahboube Bakhshali. The subspace pursuit method in sparse optimization. Master's thesis, Hakim Sabzevari University, Faculty of Mathematics and Computer Science, September 2018. روش جستجوی زیرفضا در بهینه سازی تنک. URL: http://hcloud.hsu.ac.ir/index.php/s/jacmnZiPfNFpYfk.

[Don06]

David L. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289–1306, 2006. doi:10.1109/TIT.2006.871582.

[DHS00]

Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, 2 edition, November 2000. ISBN 0471056693. URL: https://file.fouladi.ir/courses/pr/books/%5BDuda%5D_PatternClassification.pdf.

[GGRR+20]

Loubna El Gueddari, Chaithya Giliyar Radhakrishna, Zaccharie Ramzi, Samuel Farrens, Sophie Starck, Antoine Grigis, Jean-Luc Starck, and Philippe Ciuciu. PySAP-MRI: a Python Package for MR Image Reconstruction. In ISMRM workshop on Data Sampling and Image Reconstruction. Sedona, AZ, United States, January 2020. URL: https://inria.hal.science/hal-02399267.

[Nem18]

Mehdi Nemati. A gradient based method for thespectral graph partitioning. Master's thesis, Hakim Sabzevari University, Faculty of Mathematics and Computer Science, September 2018. روشی مبتنی بر گرادیان برای افرازبندی طیفی گراف. URL: https://hcloud.hsu.ac.ir/index.php/s/tR53d9306yZSAc7.

[Ras16]

Farzane Rashidabadi. Image matting. Master's thesis, Hakim Sabzevari University, Faculty of Mathematics and Computer Science, January 2016. برش هوشمند تصویر. URL: http://hcloud.hsu.ac.ir/index.php/s/OaHkkTSsO8mNrk0.

[SAK19]

A.K.M.E. Saleh, M. Arashi, and B.M.G. Kibria. Theory of Ridge Regression Estimation with Applications. Wiley Series in Probability and Statistics. Wiley, 2019. ISBN 9781118644614. URL: https://books.google.com/books?id=v0KCDwAAQBAJ.

[SASN22]

A.K.M.E. Saleh, M. Arashi, R.A. Saleh, and M. Norouzirad. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning. Wiley, 2022. ISBN 9781119625391. URL: https://books.google.com/books?id=f8p6EAAAQBAJ.

[The20]

S. Theodoridis. Machine Learning: A Bayesian and Optimization Perspective. Academic Press, 2020. ISBN 9780128188040. URL: https://books.google.com/books?id=l-nEDwAAQBAJ.

Contents