CATEGORY / Stanford Machine Learning

09Nov

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Properties Of Matrix Multiplication

1. Not commutative. $A\times B \neq B\times A$
2. Associative. $(A\times B)\times C = A\times (B\times C)$

e.g. For $A \times B$ where $A$ is $m\times n$ matrix and $B$ is $n\times m$ matrix,
$A\times B$ is an $m\times m$ matrix,
$B\times A$ is an $n\times n$ matrix.

Identity matrix
Denoted as $I$ or $I_{n\times n}$
e.g.

$\begin{bmatrix} 1 & 0 & 0 \newline 0 & 1 & 0 \newline 0 & 0 & 1 \newline \end{bmatrix}$

For any matrix $A$ , $A\times I=I\times A=A$

09Nov

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Matrix Multiplication

$\begin{bmatrix} a & b \newline c & d \newline e & f \end{bmatrix} \times \begin{bmatrix} y \newline z \newline \end{bmatrix} = \begin{bmatrix} a\times y + b\times z \newline c\times y + d\times z \newline e\times y + f\times z \end{bmatrix}$

3 by 2 matrix $\times$ 2 by 1 matrix $=$ 3 by 1 matrix

$m$ by $n$ matrix $\times$ $n$ by $o$ matrix $=$ $m$ by $o$ matrix

09Nov

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Addition & Scalar Multiplication Of Matrices

Addition:

$\begin{bmatrix} a & b \newline c & d \newline \end{bmatrix} + \begin{bmatrix} w & x \newline y & z \newline \end{bmatrix} = \begin{bmatrix} a+w & b+x \newline c+y & d+z \newline \end{bmatrix}$

Scalar multiplication:

$\begin{bmatrix} a & b \newline c & d \newline \end{bmatrix} \times x = \begin{bmatrix} a\times x & b\times x \newline c\times x & d\times x \newline \end{bmatrix}$

09Nov

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Matrices & Vectors

Matrix
Matrix: rectangular array of numbers
Dimension of matrix: number of rows $\times$ number of columns
$A_{ij}$ : $i$ , $j$ entry in the $i^{th}$ row, $j^{th}$ column

e.g.

$\begin{bmatrix} a & b & c \newline d & e & f \newline g & h & i \newline j & k & l \end{bmatrix}$

dimension: $4\times3$ or $\mathbb{R^{4\times3}}$
$A_{11}=a$
$A_{32}=h$

Vector
Vector: $n\times1$ matrix
$v_{i}$ : $i^{th}$ element

e.g.

$\begin{bmatrix} a \newline b \newline c \end{bmatrix}$

dimension: 3-dimensional vector or $\mathbb{R^{3}}$
$v_{1}=a$
$v_{3}=c$

1-indexed vector:

$\begin{bmatrix} y_1 \newline y_2 \newline y_3 \end{bmatrix}$

0-indexed vector:

$\begin{bmatrix} y_0 \newline y_1 \newline y_2 \end{bmatrix}$

08Nov

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Gradient Descent

Gradient descent algorithm
repeat until convergence {
$\theta_j := \theta_j - \alpha \frac{\partial}{\partial \theta_j} J(\theta_0, \theta_1)$ (for $j=0$ and $j=1$ )
}

$\alpha$ : learning rate
$a:=b$ : assigning $b$ to $a$

Simultaneous update
temp0 := $\theta_0 - \alpha \frac{\partial}{\partial \theta_0} J(\theta_0, \theta_1)$
temp1 := $\theta_1 - \alpha \frac{\partial}{\partial \theta_1} J(\theta_0, \theta_1)$
$\theta_0$ := temp0
$\theta_1$ := temp1

Gradient descent for linear regression
repeat until convergence {

$<span class="ql-right-eqno"> </span><span class="ql-left-eqno"> </span><img src="https://teach.sg/wp-content/ql-cache/quicklatex.com-776add333c9f68e7c5d7e1045a24c150_l3.png" height="109" width="270" class="ql-img-displayed-equation quicklatex-auto-format" alt="\begin{align*} \theta_0 := \theta_0 - \alpha \frac{1}{m} \sum\limits_{i=1}^{m}(h_\theta(x_{i}) - y_{i}) \\ \theta_1 := \theta_1 - \alpha \frac{1}{m} \sum\limits_{i=1}^{m}\left((h_\theta(x_{i}) - y_{i}) x_{i}\right) \end{align*}" title="Rendered by QuickLaTeX.com"/>$

}

30Oct

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Cost Function

Linear regression: solve a minimisation problem

minimise $\theta_0, \theta_1$ for $J(\theta_0, \theta_1)$

cost function: $J(\theta_0, \theta_1) = \dfrac {1}{2m} \displaystyle \sum _{i=1}^m \left (h_\theta (x_{i}) - y_{i} \right)^2$

30Oct

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Model Representation

$m$ : number of training examples
$x$ : input variables / features
$y$ : output variables / target variables
$(x,y)$ : single training example
$(x_i,y_i)$ : $i^{th}$ training example

Training set to learning algorithm to hypothesis $h$ (based on size of house) to estimates price

$h_\theta (x) = \theta_0 + \theta_1 x$
$\theta_i$ : parameters

Linear regression in one variable = univariate linear regression

19Oct

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Unsupervised Learning

No labels and need to find structures
Clustering algorithm: Google news, social network analysis, market segmentation

19Oct

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Supervised Learning

Regression: predict continuous valued output (price)
Example: housing pricing prediction

Classification: predict discrete value output (zero or one)
Example: prediction if tumour is benign or malignant

Support vector machine: algorithm to process infinite number of features

19Oct

2016

Eugene / Learning, Stanford Machine Learning / 0 comment

Introduction To Stanford Machine Learning (Coursera)

I have started these few threads about what I have learnt from Stanford Machine Learning course on Coursera.

I highly recommend that you take up the course to learn more about Machine Learning.

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