This document provides an introduction to artificial intelligence and machine learning. It discusses key concepts like supervised vs unsupervised learning and common machine learning algorithms like linear regression, logistic regression, clustering, random forests and neural networks. It also covers example applications like price prediction, medical diagnosis and image recognition. Programming languages for machine learning like Python, R and Java are listed. The machine learning process of collecting data, selecting algorithms, building models and making predictions is summarized.

1. INTRODUCTION TO
ARTIFICIAL INTELLIGENCE
BY: ROUSSOLINI SOCRATES &
EINSTEINIA SOCRATES

2. GOOGLE VS YAHOO
AMAZON GO

3. GOOGLE / YAHOO COMPARISON

4. WHAT IS AI?

5. AI
Machine Learning
Supervised
Learning
Un-
supervised
Learning
Conventional
Algorithms
Advanced
Algorithms
Computer
Vision

6. Machine
Learning
The theory that computers can learn
without being programmed with existing
data

7. ProgrammingLanguages
Algorithms
SupervisedUnsupervised
MachineLearningModel
PREDICTION
New
observationTrain Data
Machine Learning Steps

8. Supervised
Learning
Unsupervised
Learning

9. Supervised Learning
Linear Regression
• What will the temperature be in Normal tomorrow?
• How many units of this product will sell next month?
• What price will this house sell for?

10. Logistic Regression
Binomial
Classification
• Will the customer buy this product?
• Is this email spam or ham (good one)?
• Is this review written by a customer or a robot?
Supervised Learning
Linear Regression

11. • Is this product a book, movie, or clothing?
• Is this movie a comedy, documentary, or thriller?
• What is the T-shirt of a random person S, M, L, or XL?
Logistic Regression
Binomial
Classification
Supervised Learning
Linear Regression
Multinomial
Classification

12. Linear Regression

13. Area in Sq.
Feet
# of Floors # of Rooms Price
(Label)
700 2 3 $150k
1400 3 5 $260k
1350 3 4 $255k
… … … …
… … … …
Machine Learning Model Data Table
(Linear Regression)

14. Price
Area in Square Feet
Linear Regression Model
500 1000 1500 2000 2500
50k
100k
150k
200k
250k
300k

15. Prediction using Linear Regression Model
1150
Sq. Ft
2 Floors 3 Rooms ??
Price
Area in Square Feet
500 1000 1500 2000 2500
50k
100k
150k
200k
250k
300k

16. Logistic Regression
(Binomial Classification)

17. Logistic (Binomial) Regression Model
No -0
Yes -1
Tumor size

18. Prediction using
Logistic (Binomial) Regression Model
Tumor size
No -0
Yes -1

19. Logistic (Multinomial) Regression Model
Small
Age
Medium
Large
X Large
T-ShirtSize
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

20. Clustering
Unsupervised Learning
• Data points within a group are more similar
• Data points in a group are different from other group

21. Clustering
Unsupervised Learning
Dimensionality Reduction
• Helps reduce number of features in a dataset (advanced)

22. Group - C
Group - A
Group - B
Clustering Model

23. Group - C
Group - A
Group - B
Prediction using Clustering Model

24. • Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbour (kNN)
• AdaBoost
• XGBoost
• LightGBM
Conventional
Machine Learning Algorithms
Advanced
• Dense Neural Network
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Autoencoder
• OpenCV
• Computer Vision
• Image Classification
• Image Localization
• Video Classification
• Video Localization

25. Amazon Go Video

26. Programming Languages
• R
• Python
• Java
• C, C++
• Julia
• Scala
• Spark

27. Machine Learning Steps - Review
ProgrammingLanguages
(R,Python,Java,Scala…)
Algorithms
(Conventional/Advanced)
SupervisedUnsupervised
MachineLearningModel
PREDICTION
New
observationTrain Data

28. Data Science Profession
Mathematics
& Statistics
Domain
Expertise
Computer
Programming
Data
Scientist

29. QUESTIONS?
roussolini@gmail.com
einsteinias@gmail.com