owai.com

Discriminative models.

Discriminative models directly map inputs to outputs using conditional distribution or prediction function.

• Logistic regression/maximum entropy classifiers
• Linear discriminant analysis
• Support vector machines
• Boosting
• Conditional random fields
• Linear regression
• Neural networks

Generative models.

Generative model assume a common distribution that govern by parameters and find parameters values that best fit the training data.

Notes on statistical distance measures:

1. Euclidean distance:The straight line distance between two points. Summing the square root of the squared differences between each coordinate.
2. Cosine distance: Dividing the dot product of two vectors by the product of their lengths.
3. Manhattan distance: The distance between two points measured along axes at right angles. Summing the absolute values of the difference between each coordinate.
4. Hamming distance: The number of bits which differ between two binary strings.

I like this paper on machine learning , it is by L.G. Valiant

Abstract:
Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming.We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learned using it in a reasonable (polynomial) number of steps. Although inherent algorithmic complexity appears to set serious limits to the range of concepts that can be learned, we show that there are some important nontrivial classes of propositional concepts that can be learned in a realistic sense.

A Theory of the Learnable (full paper in pdf)

The book explains why certain behaviors are not logical yet prevalent in society. By understanding these irrational behavior you as a designer can create more effective product.

Must read book for designer!

This little book is very useful for any form of design, including software design. It is short, concise, and easy to read. It make you think about things that you never thought about in normal software design process.

Just found out about Google Chart API, it lets you dynamically generate chart. pretty cool! I generated my personal profile chart using it

http://code.google.com/apis/chart

Playing around with google spread sheet function importXML() lead me to create a graph of a treasury yield curve for 2008. The data source is US Treasury web site.

Just finish reading the book, it is very interesting - it contain following information.

1. recognition prime decision modle.
2. the power of intuition.
3. the power of mental simulation.
4. power to spot leverage point.
5. non linear spect of problem solving.
6. power to see the invisible.
7. the power of stories.