Friday, 14 September 2012

The Go to Class Challenge - part 1

MoCh - Challenge #6 September 2012 - The Go to Class Challenge

Go to class.

The Challenge
This months "Go to Class Challenge" (read more about it at the bottom of this page) is quite a difficult challenge. Mainly because the lack of time, however as well due to the nature of the subject I'm studying. There is a fair bit of math in machine learning, and unfortunately I've forgotten most of my university mathematics. Hence I spend much time getting up to speed on subjects that to many is straight forward (like linear algebra). Another challenge is that I started studying one week after the course had started, and I've not managed to catch up. Due to the deadlines for the weekly programming exercises and review questions I've had a couple of late nights. Life now consists of work, exercise and studies. Nothing more.

The subject is highly interesting. The way it is taught is also very good. The lecturer in charge, Mr Andrew Ng is doing a great job of passing on the principles of machine learning. In the next update on this challenge I will write more about the fantastic service I'm using to take this course. To view some of my work process check out my morphogenetically blog.




A typical evening in front of the screen.

Lecture notes.

About the Course
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
(src: from the course website, course materials can be viewed on this page).

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