Course Preview

Here you will find some snippets, snapshots, and my podcasting adventures for courses that I have taught and I'm teaching along the way. I believe teaching is dynamic and while there is value to foundational knowledge, advanced engineering courses should incorporate feedback from the real world. Thus, in my teaching expeditions, you will see an evolving nexus of bottom-up algorithmic foundations and top-down domain-inspired data engineering and distributed computing essentials. I also blend in podcasts, videocasts, research papers, and blogs in my teaching. All of this, in addition to synchronous lectures, which prime me to learn and evolve as a better teacher!

This smorgasbord of my teaching expeditions will only get more diverse in the future as the world around us adapts. The diversity is what will make complex and evolving data science and data engineering more available and usable by domain scientists, especially in my domains of Genomics and IoT/Cloud/Edge Computing.

Video 1

Roughly 18 minutes [ML intro], covering basics of supervised and unsupervised machine learning, Portland housing market data set, modeling performance

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Video 2

Roughly 14 minutes [ML intro-part 2], covering dimensionality reduction, computational complexity, interpretability of ML models

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Video 3

[Advanced on-device ML]

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Podcast: Non-Linear Computation

Episode 1: What's in an algorithm?