흥미로운 IT

Supervised learning

-classification vs regression(contiguous variables)

Unsupervised learning

-no answers given to the algorithm ⇒ computer automatically analyze

-cocktail party problem ⇒ 2 audio recordings → separate out the two voices ⇒ can be done with single line of code

⇒ [W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x’);

⇒ use “Octave” or “Matlab” ⇒ it’s faster

[Linear Regression]

Model Representation

-supervised learning has training set

-training set → learning algorithm

* hypothesis:

Cost Function

⇒ Goal: minimize J(θ0 , θ1) ⇒ global minimum

⇒ use contour plots/figures for visualization

⇒ linear line of h(x) is converted to a single point in cost function graph

Gradient Descent Algorithm

If is α too small ⇒ gradient descent can be slow (alpha = step size)

If is α too big ⇒ gradient descent fail to converge, or even diverge

α rate doesn’t need to decrease →automatically take smaller steps

Batch Gradient Descent: every step needs to calculate all training sets in batches

Review:

Although there is difficulty in understanding the whole process, particularly the gradient descent equation, I am fairly able to get the big picture and the important concepts of machine learning regarding supervised/unsupervised learning, model representation, cost function, and gradient descent algorithm.

I am currently able to follow the contents and able to solve the quiz in Coursera for each lecture without much difficulty, yet!