[ML] Basic terms

Confused about "h(x) = theta' * x" vs. "h(x) = X * theta?"


So the videos (and PDF files) are organized toward processing one training example at a time.
The course uses column vectors (in most cases), so h (a scalar for one training example) is theta' * x.

Lower-case x typically indicates a single training example.
即:
    xxx
    xxx * X = b
    xxx

Always use X as a (matrix) of all training examples,
    with each example as a row, and the features as columns.

m = examples
n = features

x
x * X 即 三個examples x乘上一個feature X
x

h (a vector of all the hypothesis values for the entire training set) as X * theta, with dimensions of (m x 1).

**Throughout this course, dimensional analysis is your friend.


--
Supervise learning:
    Give correct data set to the question.
    Using the algorithm to find the relative data according the input correct data.
 
--Regression: Predict continuous value output.
--Classification Problem: Predict discrete value output.


Unsupervise learning (cluster learning):
    Data set has no labels. Just find a structure to present it.
 
-- cocktail party problem


------------------
Training set:
m = number of training set
x's = input variable  / features
y's = output variable / target variable

(x, y) - one training example
(xi, yi) -  i-th training example

Assignment:
a := 1

Truth association:
a = a

alpha: learing rate
derivative term: partial derivative

Batch gradient descent:
    Each step of gradient descent uses all the training examples.