This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:
Ronny De Winter completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
Excellent caltech course which runs in parallel with the on-site university class.
Good theoretical coverage and applied programming exercises. Highly dedicated teacher and teaching assistants, closely following up the discussion forum. recommended for every serious data scientist. One of the best MOOCs I've completed. More elaborated than Andrew Ng's intro to Machine Learning.
HChan completed this course, spending 12 hours a week on it and found the course difficulty to be hard.
The best online machine learning course I've taken (I've completed courses by Andrew Ng as well as Hastie and Tibshirani et al), this course covers rigorous theory as well as practical aspects, setting you up for a very solid foundation for future study in machine learning. Assignments are challenging and really require you to understand and engage with the material. Prof Abu-Mostafa's teaching quality is amazing and even highly complex concepts are clearly presented.