1. Introduction to Data Science: Lecture 1
March 15, 2017
Introduction to Data Science: Lecture 1
Dr. Amitai Armon

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:
Introduction to Machine Learning
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. Intel AdvanceD Analytics: A Little about US
OUR MISSION
Use data science for upgrading Intel’s operations
Help Intel win the data-science market
Operational
Excellence
Technology
Breakthrough
Design
Manufacturing
Marketing & Sales
Deep Learning Products
Health wearables platform

7. Intel AdvanceD Analytics: A Little about US
OUR MISSION
Use data science for upgrading Intel’s operations
Help Intel win the data-science market
Data Science Summit conferencesi n San Francisco and Jerusalem
CONTRIBUTIONS TO Data-Science Community
Industry collaborations
Helping Intel VC Investments
Academy collaborations

8. Machine Learning is Everywhere…
Handwriting Recognition
Speech Recognition
Automatic translation
Credit-card fraud detection
Image Classification
Social Networks Analysis (community detection)
Movie / product / article recommendations
Autonomous cars ….

9. Winning in Jeopardy

10. Winning Against Go Champion

11. Answering Visual Questions
Kan et al., 2015

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

13. What is Machine Learning?
Wikipedia: Machine Learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Tom Mitchell (1998): A computer program is said to learn from experience, with respect to some task and some performance measure if its performance, as measured by the performance measure, improves with experience.

14. “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

15. Learning from Examples
What is “Dangerous”?

16. Learning from Examples
So are these items dangerous or not?
It’s important to have enough diverse examples, not all ‘same type’

17. Typical Machine Learning Tasks
No two machine learning tasks are identical, but still there are common prototypes:
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 the “child learning” example

18. Typical Machine Learning Tasks
Supervised Learning
Estimate an unknown result, given explicit values of some explaining variables (“features”).
Estimate it based on a set of observations for which both the result and the explaining variables are known (“training set”).
This may be prediction (“it’s difficult to give forecasts, especially about the future”) or estimation.

19. Typical Machine Learning Tasks
Supervised Learning
Example 1: What will be the annual spend of my clients?
The unknown result: the annual spend (this is a prediction)
Explaining variables (“features”): Client’s details (e.g., domain, size, purchase history)
Training set: The annual spend in past years, with respect to the client’s data available so far (at the beginning of that year)

20. Typical Machine Learning Tasks
Supervised Learning
Example 2: What is the activity currently performed by a Parkinson’s patient?
The unknown result: the activity (this is not a prediction – the fact exists, we simply don’t know it)
Explaining variables: Various features that are extracted from sensory data on the patient’s body (accelerometers, gyro, compass)
Training set: features and the corresponding activity labeling (we must have a labeled training set)

21. Typical Machine Learning Tasks
Supervised Learning
Two main tasks are considered in Supervised Learning:
Regression: the unknown result is a numerical value (e.g., annual spend)
Classification: the unknown result is a class relation (e.g., the activity)
Regression and classification have different objective measures, and often different algorithms.

22. Typical Machine Learning Tasks
Unsupervised Learning Given explicit values of some variables (pre-defined set), extract interesting patterns that appear in the data, or provide an insightful representation of the data inherent distribution.

23. Typical Machine Learning Tasks
Unsupervised Learning
Example: Market Segmentation
Input data: Clients information
Objective: Identify what types of clients are there?
This objective is known as ‘Clustering’ or ‘Cluster Analysis‘

24. Typical Machine Learning Tasks
Reinforcement Learning Reinforcement Learning is learning how to best react to situations through trial and error. In some sense reinforcement learning is the first way of learning we think of. Example: TD Gammon

25. Few Supervised Learning Approaches

26. Supervised Learning X1 X2 X3 … Xn-2 Xn-1 Xn Y x1,1 x2,1 x3,1 xn-2,1.
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
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,…

27. Lazy Learner: k-Nearest Neighbors
Identifying spam s
What should be k?
Which distance measure should be used?
Computation
K=3
Length
New Recipients

28. Linear Classifiers
How would you classify this data? X1 X2

29. Linear Classifiers
How would you classify this data? X1 X2

30. Linear Classifiers X1 X2 Any of these would be fine..
..but which is best? X1 X2

31. Maximum Margin Email Length New Recipients
Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a data point.
Length
New Recipients

32. Maximum Margin Email Length New Recipients
The maximum margin linear classifier is the linear classifier with the maximum margin.
This is found by the SVM algorithm (Support Vector Machine)
Length
New Recipients

33. Decision tree A flow-chart-like tree structure
Internal node denotes a test on one of the features
Branch represents an outcome of the test
Leaf nodes represent class labels

34. DEEP NEURAL NETWORKS
Bengio, 2009

35. Block Diagram of a Supervised Learning System
Hypothesis Space
Training
Set
Learning Alg. h
Estimated
εg(h)
Testing
h(x)≠ct(x)
Test Set

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

37. Regression Learning Example

38. 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

39. CRISP-DM Methodology
CRISP-DM stands for Cross Industry Standard Process for Data Mining
Conceived in by SPSS, Teradata, Daimler, NCR and OHRA
IBM is the primary corporation that embraced and incorporated it in its SPSS Modeler product
CRISP-DM defines a methodology for ML/DM projects

40. CRISP-DM 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

41. Summary We briefly discussed today: What is Machine Learning
Typical Machine Learning tasks
Supervised Learning:
Learning means Generalization
Overfitting and Underfitting
Simple learning paradigms
Training vs. Testing
Classification and Regression
CRISP-DM

42. Introduction to
Data Science
Questions?

43. Thank you!