basics-of-AI-and-ML

This presentation provides an overview of Machine Learning, including its history, types, algorithms, and applications. It covers topics such as supervised and unsupervised learning,linear regression, classification, clustering, model selection, evaluation, and deployment.

This repo is a great resource for beginners and experienced practitioners alike to learn about this important field.

this presentation created using a platform called marp check https://github.com/marp-team/marp

Created by Ahmadreza Anaami

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AI: From the pages of science, to the future of our lives.

by Ahmadreza Anaami

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Goals

In this lab you will:

• have a brief introduction on AI and ML
  • basic concept of machine learning
• main algorithm
  • supervised
    • regression
    • classification
  • unsupervised
• Learn to implement linear regression

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machine learning

Subfield of AI in order to make intelligent machine

Field of study that gives the computers the ability to learn without explicitly programmed

• Arthur samuel 1959

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Supervised learning

• learn from being given right answers
  • correct pairs of input (x) and output (y)

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input	output	application
email	spam?(0,1)
audio	text transcript
English	Spanish
ad-User	click?(0,1)
image-radar	position of other car
image of phone	defect?(0,1)

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Regression

predict a number from infinitely many possible outputs

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Classification

many inputs some outputs called categories

Size	diagnosis
6	1
8	1
2	0
5	0
1	0
7	1
5.6	1
12	1
3.5	0

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Recap

In every living man a child is hidden that wants to play

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Unsupervised learning

Find something intreating in (unlabeled) Data

• Clustering
• anomaly detection
• Dimensionality reduction

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linear regression

$$f_{w,b}(x) = wx + b$$

w , b > parameters , weight x > single feature

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Cost function

$$J(w,b) = \frac{1}{2m} \sum\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2$$

where

$$yHat = f_{w,b}(x^{(i)}) = wx^{(i)} + b $$

m = number of example

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Example

choose w to minimize J(w)

$$yHat = f_{w}(x^{(i)}) = wx^{(i)} $$

$$J(w) = \frac{1}{2m} \sum\limits_{i = 0}^{m-1} (f_{w}(x^{(i)}) - y^{(i)})^2$$

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Gradient descent

a more systematic way to minimize j(w,b)

$$\newline\begin{align*} \text{repeat}&\text{ until convergence:} ; \lbrace \newline ; w &= w - \alpha \frac{\partial }{\partial w} J(w,b) ; \newline \newline b &= b - \alpha \frac{\partial }{\partial b} J(w,b) \newline \rbrace \end{align*}$$

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Derivative of the Cost func

$$J(w,b) = \frac{1}{2m} \sum\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2$$

$$ \begin{align} \frac{\partial }{\partial w}J(w,b) &= \frac{1}{m} \sum\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})x^{(i)} \\ \frac{\partial }{\partial b}J(w,b) &= \frac{1}{m} \sum\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)}) \\ \end{align} $$

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optional

if alpha is too small :

if alpha is too big :

$$\alpha \frac{\partial }{\partial w} J(w,b)$$

calculate Derivative : ☻

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Lets get our hands dirty

• step 1
  • read our data
• step 2
  • write our functions
    • calculate yHat
    • calculate cost
    • calculate gradient
    • implement gradient descent
• step 3
  • congratulations