I’m at university again!


What about taking knowledge from one of the best universities in the world in the middle of hot California?

After a research, I found Machine Learning Coursera course for people who’d like to know a foundation theory behind machine learning. The course is made by professor Andrew Ng, well-known in the machine learning field. The course is split into 11 weeks. It’s a mixture of videos, text lectures, short tests and programming challenges. Coursera estimates it should take around 52 hours to get the course done. It seems like there’s a lot of math out there, which to be honest, isn’t the best friend of mine, but who said it will be easy?

Because of the nature of an e-learning course, I won’t be able to see Stanford, its neighbourhood, and fellow students. I’m excited about studying ‘at’ Stanford University anyway 😀

If you read my about section, you probably know I’ve already graduated university and got the Master of Science in CS. Although I had a machine learning course back then, it wasn’t satisfying because no side made enough effort. First of all – me. I wasn’t interested and aware enough to listen carefully and take the knowledge that university wanted to give me. On the other hand, the way how they taught us machine learning would be called ‘boring’ by a majority of you. What’s even worse, they didn’t show real life cases where it might be used. Why the hell would you show the importance of classification problem using it to separate red triangles from yellow triangles? Wouldn’t it be better, starting from the very beginning, to show it as a prediction tool to distinct malicious and benign tumour? Wouldn’t it be better to explain how even a simple linear function might be helpful to save people’s lives? I know some people are smart enough to take a generalised model and find usages of it in different contexts and real situations. But it’s not that obvious to everyone at uni times. I was disappointed.

Anyway. My machine learning mind map will be updated during the course with notes taken directly from lectures filled out with my comments. I’ll try to cover more interesting subjects as separate blog posts. However, the main focus should be placed on playing with different machine learning algorithms in practice. To make it synced with the subject of the blog, I’ll always try to show examples in a music context.

By the way. Have you thought about how e-learning platforms try to integrate students together? Here is a proposition directly from my current course:

Yep. It’s almost like sitting in a pub, drinking a beer and having a good conversation after classes 😉



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