Hands-on lecture development for University of Michigan's undergrad ML course and a WIP EM notebook
I've been offline in my logging of learning for a while and it's in part because I, in addition to my regular job (where I'm learning of practical stuff about running NN toolkits, especially MXNet), I ended up helping out with the University of Michigan's ML class. It was a great opportunity to learn some concepts, but also took up most of my free learning time, including the time to write up my learning!
In any case, I figured I'd link to the notebooks I helped develop, they are available on the courses github repo. I helped with hands-on lectures 12-21, covering:
- Ensemble learning with bagging
- Evaluating models with learning curves
- Unsupervised learning and Principal Components Analysis (PCA)
- Bayesian networks
- Latent Variable models and Expectation Maximization
- Hidden Markov Models, the forward-backward algorithm and the Baum-Welch algorithm (EM for HMMs)
- Introduction to neural networks: the backpropogation algorithm and some of the basics on convolutional neural networks
I'm not sure how instructive they will be without having also read through the lecture notes and perhaps attended the lectures, but perhaps there's some good stuff in there for others.
For some of these I already was familiar with the material and was able pull something together pretty easily (e.g decision trees, bagging, learning curves, PCA) but towards the end it was quite challenging: each week I both needed to learn and develop an exercise for a topic, including bayesian networks, EM and HMMs.
One concept that was particularly vexing to me was expectation maximization. I've followed up on the original hands-on lecture to go a bit more deeply into it with an in progress notebook (that is currently unlisted). I will say more once I've wrapped it up, but I hope to make a nice standalone notebook that will help others get up to speed more quickly.