Ricardo Calix

My Books

I have been teaching cyber security and machine learning topics since 2012. Over the years I have had the opportunity to combine topics from both areas in my research. Many of the chapters in this book are a direct result of this effort. My goal with this book is to cover AI for cyber security, and AI assurance. I use the term AI although I mainly mean machine learning. AI for cyber security refers to the use of ML algorithms to provide defense for information systems. Here I will cover topics such as malware detection, intrusion detection, phishing detection, AI for cryptography, and privacy preserving defense techniques. The topic of AI assurance relates to creating secure and reliable AI systems. Here I will cover topics such as AI auditing and explainability, bias testing, adversarial attacks, etc. As usual, my books are applied and I will use Python and PyTorch extensively. I hope you enjoy the book!

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Natural Language Processing, Text-to-Scene processing, Machine learning, Deep Learning, Transfer Learning, GANs, and Reinforecement Learning. So far, I have applied AI to multimedia analysis, animation, cyber security, industrial plant forecasting, AI auditing, agriculture and genetics, etc. I am always interested in tackling new challenges.

Practical Deep Learning (i.e. Transfer Learning)

Spring 2024

This is a course in Deep Learning

Machine Learning Foundations

Spring 2024

This course provides a basic introduction to the machine learning pipeline and related concepts. Topics covered include: Machine learning uses and applications; data set requirements; data pre-processing; data annotation, and validation; data representation formats; features and feature representation and extraction; the vector space model; traditional machine learning algorithms; machine learning algorithms and programming; ML evaluation methods; introduction to deep learning algorithms; big data; reinforcement learning; Unsupervised learning; statistical significance analysis; and other special topics.