Happy holidays! Have just completed the exceptionally difficult and rewarding course on artificial intelligence, just as my new role involved putting a healthcare data product into production (press release here). The timing could not have been better. The combination of both led to late nights (due to work) and weekends completely at home (due to study).
Why take this course?
I was curious about how artificial intelligence would be defined in a formal education syllabus. In my line of work, the term “Artificial Intelligence” is greatly overhyped, with snake oil salesmen painting pictures of machines that learn on their own, even without any new data, sometimes, without data at all. There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours).
Against this context, I was interested to know how a top CS and Engineering college taught AI. To my surprise, it included topics such as adversarial search (i.e., game playing), search, constraint satisfaction, logic, optimzation, Bayes networks, just to name a few. This increased my excitement in learning about the fundamentals of using math and logic to solve difficult problems.
In addition, the course had a very good reviews (4.2 / 5, one of the highest), with a difficulty of 4.3 / 5, and average workload of 23 hours a week. Based on these three metrics, AI was rated better, more difficult, and requiring more time than Machine Learning, Reinforcement Learning, and Computer Vision—challenge accepted! It was one hell of a ride, and I learnt a lot. However, if I had to go back in time, I’m not sure if I would want to put myself through it again.
What’s the course like?
The course is pretty close to the real deal on AI education. Readings are based on the quintessential AI textbook “Artificial Intelligence”, co-authored by Stuart Russell and Peter Norvig. The latter is a former Google Search Director who also guest lectures on Search and Bayes Nets. Another guest lecturer is Sebastian Thrun, founder of Udacity and Google X’s self-driving car program. The main lecturer, Thad Starner, is an entrance examiner for the AI PhD program and draws from his industry experience at Google (where he led the Google Glass development) when structuring assignments.