By Harry Keller
Editor, Science Education
[Note: Harry, who holds a BS in chemistry from the California Institute of Techology and a PhD in analytical chemistry from Columbia University, is sharing his first MOOC experience in this series. See part 1, 2, 3 and 4. -Editor]
The End of My First MOOC Journey
As an audit student, my motivation to finish is not great. I have to ask if drop-out rates mean not taking the final exam because I never intended to punish myself that way when I began. I already have a very good doctorate in science and have no incentive to acquire more initials after my name today.
The last two lectures of the course were both quite technical and, once you penetrate the jargon, quite illuminating. They brought me to the state of the art in machine learning: SVM and RBF.
I was introduced to machine learning decades ago and was interested enough to obtain a book on it, the only book available at the time. This book was heavy on theory and not very useful to a practitioner. Machine learning in those days consisted of just one approach, known today as PLA or Perceptron Learning Algorithm. It was adequate for simpler learning situations and was adjusted to work in others but was not really sufficient.
The machine learning universe expanded with the advent of neural networks, a learning concept that sprang from biological work at the time. Neural networks are still used but not very often.
Today, support vector machines (SVM) and radial basis functions (RBF) represent where machine learning lies. The former requires an advanced mathematics known as quadratic regression (QR). The latter only requires matrix manipulation and iterative processing, both readily available with software. Continue reading
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