Bayesian Methods for Machine Learning

Checkout the syllabus for this Coursera course. Too bad I just enrolled in it, so I can't give you my personal review. If you're still interested in a few weeks, feel free to pm me. Bayesian ML

If you’re into courses like I am then here’s a course covering it. I haven’t gotten to it yet, but it’s the next course in the specialization for me

Looks like there’s an entire Coursera course on it. I just think I might take it!

I think that Coursera's Bayesian Methods in Machine Learning does a pretty good job at introducing VI.

They introduce it after they do a week on EM algorithm. They emphasise continuity between EM and more elaborate models, one of them VI. In one of the assignments they ask you to implement VAE (in tf or Keras, I don't remember).

take a look at course by coursera :Bayesian Methods in Machine Learning.

https://www.coursera.org/learn/bayesian-methods-in-machine-learning

Maybe I am biased but why would you learn on your own what you can learn in 2 years with teachers and researchers ?

It is okay to not do a masters degree if you want to do something not that technical, but if you're interested in Machine Learning and Datascience why not have a master's degree in that specific field ? You can't go faster nor deeper on your own than with teachers who pre-select the most important concepts. You can even do both : study for you courses AND strengthen the concepts on your own around them.

For the courses, I'd suggest :

• Machine Learning on Coursera to start it off : https://www.coursera.org/learn/machine-learning
• Machine learning on EdX for more advanced topics https://www.edx.org/course/machine-learning
• I am following this mooc on Bayesian methods for ML : https://www.coursera.org/learn/bayesian-methods-in-machine-learning and though everything is not always clear, I find it really interesting and quite challenging compared to other moocs.

The last two are the only technical MOOCS I found on ML. I don't know if anyone has other resources !

For the books :

• If you feel like you're lacking in statistics : the book All of Statistics is good http://egrcc.github.io/docs/math/all-of-statistics.pdf
• An Introduction to Statistical Learning : https://www.statlearning.com
• The more technical Elements of Statistical Learning https://web.stanford.edu/\~hastie/Papers/ESLII.pdf
• I haven't worked a lot with Deep Learning yet so I don't have many resources on the topic.

Be sure to watch videos (I think this year's run will also be recorded) in the meantime! It'd also help to familiarise yourself with classical bayesian methods (bayesian inference, mcmc, EM, variational inference), for example, through our course on Coursera.

The classes I've complete are:

Machine Learning (https://www.coursera.org/learn/machine-learning/)

Introduction to Deep Learning (https://www.coursera.org/learn/intro-to-deep-learning/)

and I'm about half way through:

Bayesian Methods for Machine Learning (https://www.coursera.org/learn/bayesian-methods-in-machine-learning/)

I think these two will be very useful:

Bayesian statistics course: https://www.coursera.org/learn/bayesian-statistics

Bayesian methods for ML: https://www.coursera.org/learn/bayesian-methods-in-machine-learning