Zooming Out (meta)

I’ve been thinking about my overall strategy for learning machine learning and this led to googling “machine learning curriculum.” I first discovered Karl Rosaen’s blog; he had also quit his job to embark on a learning sabbatical. Incidentally I am taking the same Statistics MOOC as he mentions in his “Phase 1.” He wrote

Having the goal of “learning machine learning” is daunting.

Each phase should include at least one track that builds practical skills and one track focused on theoretical foundations.

The first quote above is what sent me a-googling. The second also makes sense to me and indeed the Stanford MOOC and the Udemy Python/ML course have been complementing nicely. I also noted that his “Phase 1” took him 5 months to complete. I’ve been wondering how long the Statsprob course will take, so this is one data point. I intend to complete it sooner than that. He also wrote

Additionally, it’s always worth surveying the field at your current level of fluency to be on the look out for the next phase of studies and to continue to build a mental map of interconnected topics that may be prerequisites for the techniques and applications that you find most exciting.

Finally, I found this quote enlightening and similarly in accord with my approach:

Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid.

–Wasserman, preface to All of Statistics

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