1. Introduction to Data Science Lesson 1
Intro: Dr. Amitai Armon, Chief Data Scientist, Intel Advanced Analytics

2. Administrative Details
Course lecturer: Prof. Tova Milo
Course teaching assistant: Slava Novgorodov
Grade structure:
30% Exercises
70% Final Exam
Course website:

3. Course Topics
This course will provide a practical introduction to machine-learning and big data
Main topics of the classes:
A brief introduction to Machine Learning & Artificial Intelligence
Data Understanding and Data Preparation
Feature Selection and Model Evaluation
Supervised Modeling
Unsupervised Modeling
Deep Learning
Introduction to Big Data
Spark
NoSQL databases
Spark Streaming

4. Exercises There will be four exercises during the course
The last exercise will be bigger
Exercises will be in Python
Submission is in pairs
See the course website:

5. Administrative Details
Questions?

6. A little bit about us: Machine Learning & Artificial Intelligence at Intel
We Enable the ML & AI Market
We Use ML & AI to Make Smart Products
We Use AI to Upgrade Our Own Operations

7. Intel’s Advanced Analytics department
A group of 120 Data-Scientists, Big Data Developers and Product Experts
located in Israel
Sales &
Marketing
Design
Radically improving operations
manufacturing
Effective validation
Lower cost, higher quality
Increasing ROI and scale
Health
Analytics
Building smart products &
Enabling the AI Market
Embedded AI
Industrial AI
Processors that learn
Smart clinical trials
IOT platform for factories

8. Machine Learning & Artificial Intelligence ARE Everywhere…
Handwriting recognition
Automatic translation
Recommendations of products/websites
Credit-card fraud detection
Speech recognition
Algo-trading
Personal assistant applications
Autonomous cars ….

9. Answering Visual Questions
Kan et al., 2015

10. Dialogue (“Turing Test”)
Google chatbot, 2015

11. What is Artificial Intelligence?
a machine mimics "cognitive" functions that humans associate with other human minds [Wikipedia]
As machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, leading to the saying "AI is whatever hasn't been done yet”

12. What is Machine Learning?
A branch of artificial intelligence, concerns the construction and study of systems that can learn from data. [Wikipedia]
Alternative definition: Constructing systems that use data to improve in achieving a goal X1 X2
More complex input data and tasks often require more sophisticated models
Classification is just one task type, other examples are regression and recommendations

13. Machine Learning modeling techniques
Machine Learning: Using DATA to learn (define) how to achieve a goal
Machine Learning
Neural Networks X2 X1
Deep
Learning
There are dozens of Machine-Learning modeling methods

14. “A Brief History of Machine-Learning”
Graph created by Eren Golge, published in Kdnuggets, Oct. 2014

15. Typical Machine Learning Tasks
Supervised Learning
Learning from labeled examples (for which the answer is known)
Unsupervised Learning
Learning from unlabeled examples (for which the answer is unknown)
Semi-supervised Learning
Learning from both labeled and unlabeled examples
Active Learning
Learning while interactively querying for labels of examples
Reinforcement Learning
Learning by trial and feedback, like “child learning”

16. Illustration: Supervised learning
Step 1: Training In Data Center – Over Hours/Days/Weeks
Step 2: Inference End point or Data Center - Instantaneous
Person
Lots of labeled input data
New input from camera and sensors
Create model
Trained Model
Trained neural network model
97% person
2% traffic light
Output:
Trained Model
Output: Classification

17. Supervised Learning Features X1,…Xn Label X1 X2 X3 … Xn-2 Xn-1 Xn Y.
x1,m-1
x2,m-1
x3,m-1
xn-2,m-1
xn-1,m-1
xn,m-1
ym-1
x1,m
x2,m
x3,m
xn-2,m
xn-1,m
xn,m ym
Samples 1,…,m
Uses a set of labeled examples with known answer (“training set”)
Success is evaluated on a separate set of examples (“test set”). Various success criteria may be considered:
For classification: Accuracy, Recall, Precision…
For regression: MSE, RMSE,…

18. “Child Learning” Action Reaction Lesson Touching hot stove aching hand
Do not touch again
Playing with toys
Fun
Continue playing
Running in to the road
Screaming parent
Don’t run to roads
Running in the house
Run in the house
Eating chocolate
Search for chocolate
Eating too much chocolate
Stomach ache
Don’t eat too much
Saying bla bla
No Reaction
Try variations
Saying daddy
Overexcited parents
Do that again

19. Evaluating What Has Been Learned
Test set
2. Cross Validation
Confusion Matrix
Classified As
Red
Blue 1 7 5
Actual

20. Regression Learning Example

21. Overfitting and Underfitting
Overfitting: The model learns the training set too well – it over fits the training set such that it cannot generalize to new instances.
Underfitting: the model is too simple, both training and test errors are large

22. CRISP-DM Data Mining Methodology
CRISP-DM breaks the process of data mining into six major phases
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
The sequence of the phases is not strict and moving back and forth between different phases may be required

23. Course Topics Overview
A brief introduction to Machine Learning & Artificial Intelligence
Data Understanding and Data Preparation
Feature Selection and Model Evaluation
Supervised Modeling
Unsupervised Modeling
Deep Learning
Introduction to Big Data
Spark
NoSQL databases
Spark Streaming

24. Brief Introduction to ML & AI
Questions?