Progress updates and sudden news

Sudden news: Have to go to the US for approximately 2 weeks, so any previously planned progress will probably be impeded.
US trip: 23 Dec to 6 Jan (15 days)

 For the past few days: most work is focused on Andrew Ng's Machine Learning Course.

 So far, the area of Machine Learning focused on is "Supervised Learning", which (I guess) arguably is the part which contains simpler algorithms as opposed to "Unsupervised Learning".

 There was a heavy emphasis on regression, especially linear regression, and the associated cost function.

 The cost function is denoted as J(Θ), where Θ is a vector of the associated features (or factors affecting) of the function. The purpose of the cost function is to measure how much the data points deviates from the line of regression.

 To determine the equation of the regression line, also called the hypothesis function h(Θ), one of the methods used is to use "Gradient Descent", there was a lot of details describing the implementation of gradient descent too. The idea is just to find the derivative of the cost function output of a randomly initialized regression line. Then, slowly adjust the regression line towards the optima by using multiplying this derivative result with the learning rate, α.

 Another notable benefit that we have gained from the course so far is learning the Octave programming language, and which also serves as a programming platform mainly used for prototyping programs and concepts, ideal for this course. With our programming and math foundation, Octave has been very manageable in terms of its difficulty and also fun to use. It is a very high level language with many really convenient functions. 

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