This document provides an introduction to machine learning, including definitions of key concepts and methods. It discusses supervised learning techniques like linear regression, logistic regression, decision trees, KNN, and SVM. Unsupervised learning techniques covered are K-means clustering and PCA. Reinforcement learning and deep learning using artificial neural networks are also introduced. Examples of applications in various domains like economy, medicine, gaming and more are provided. Popular programming tools for machine learning like Python, MATLAB, Java, C/C++ and R are listed.

1. Introduction to Machine learning
By: Behnam Farsi
M.A.Sc ,CIISE Department
May, 2020

2. Contents
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1. Introduction
2. Python
3. Machine Learning and Different Methods
4. Deep Learning

3. Introduction
Machine learning is the study of algorithms that improve
their performance, at some task, with experience (TOM
MITCHELL)
� Economy
� Medical
� Gaming
� Human behaviour
� Load Forecasting
� Industry
� Climate Change
� Autonomous Cars
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4. AI
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5. Tools
•Python
•MATLAB
•JAVA
•C/C++
•R
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6. Machine Learning
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Supervised Learning Reinforcement Learning
Unsupervised Learning Semi-Supervised Learning

7. Supervised Learning
Supervised learning is the machine learning task of learning a function that
maps an input to an output based on example input-output pairs.
•Train The Model
•Test The model
•Accuracy ( Classification_report,…)
•Loss(MSE, MAE, …)
•-Repeat
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8. Supervised
Learning
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• Linear Regression
Regression
• 𝐾𝑁𝑁
Classification

9. Classification VS Regression
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10. Supervised Learning
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Linear Regression
Logistic Regression
Decision Tree
K Nearest Neighbor
Support Vector Machine (SVM)
Random Forest

11. Classification
KNN:
Store Example
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12. Dataset for Classification
Iris Dataset:
Setosa, Versicolor, Virginica
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13. Data Mining
Preprocessing:
1. Analysis the data
2. Using Training, Test
3. Normalize
Prediction:
1. Overfitting and Underfitting Problems
2. Accuracy
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14. Linear Regression
Y= b+mX
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15. Dataset for Regression
Boston Dataset
Data
Target: Price
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16. Unsupervised Learning
Unsupervised learning is a type of machine learning that looks for previously
undetected patterns in a data set with no pre-existing labels and with a
minimum of human supervision.
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17. Unsupervised
Learning
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• KMEANS
Clustering
• 𝑃𝐶𝐴
Dimensionality Reduction

18. K-MEANS
k-means clustering is a method, that aims to partition n observations into k
clusters in which each observation belongs to the cluster with the nearest mean
(cluster centers or cluster centroid), serving as a prototype of the cluster.
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19. K-MEANS
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21. Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning concerned with how
software agents ought to take actions in an environment in order to maximize
the notion of cumulative reward.
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22. Deep Learning
Deep learning is an artificial intelligence function that imitates the workings of
the human brain in processing data and creating patterns for use in decision
making.
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23. ANN
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24. Deep Learning
Using Artificial Neural Networks(ANN)
-Y=Sigma(wX+b)
-Activation Function:
Relu, Sigmoid, Softmax
- Back Propagation
-Dropout Technique
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25. Machine Learning VS Deep Learning
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26. 3