Did you ever ask yourself how Machine Learning works as a system? In this session we will take a look at the science behind Machine Learning systems. We will decrypt the base mathematical concepts that make systems like these work. Once you attend this session you will discover how polynomial expression can predict the future based on historical data.

1. 1
THE SCIENCE BEHIND
MACHINE LEARNING
RADU VUNVULEA, NOVEMBER 2017

2. RADU VUNVULEA
Technology Enthusiast
Dreamer
Microsoft Azure MVP
Speaker & Trainer
Writer & Blogger
Idealist Software
Architecture Crafter

3. PURPOSE
UNDERSTAND WHAT IS THE MAGIC
BEHIND MACHINE LEARNING

4. CORE CONCEPTS

5. Applied Artificial Intelligence
• Train and design for a specific task, like
maneuver an autonomous vehicle or
intelligently trade stocks and shares.
Generalized Artificial Intelligence
• A system that can handle any kind of task.
Learn by himself to drive a car and later on to
play on the stock market. It’s the current state-
of-the-art system.

6. “Machine learning is a field of computer
science that gives computers the ability to
learn without being explicitly programmed”
Arthur Samuel, 1959

7. Artificial
Intelligence
Machine
Learning

8. Artificial Intelligence
• Artificial Intelligence is the broader concept of
machines being able to carry out tasks in a way
that we would consider “smart”
Machine Learning
• Machine Learning is a current application of AI
based around the idea that we should really
just be able to give machines access to data
and let them learn for themselves

9. 9
9
Machine
Learning
Teaching machines to
learn by themselves
High amount of
data that is stored
Obtain valuable data
insights
WHAT SUPPORTS MACHINE LEARNING

10. MACHINE LEARNING

11. 11
11
CATEGORIZE AND GROUPING

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MACHINE LEARNING ALGORITHMS CATEGORIES
Supervised Learning Unsupervised Learning
Bedrooms Sq. feet Neighborhood Sale price
3 2000 Normaltown 250.000 $
2 800 Hipsterton 300.000 $
2 850 Normaltown 150.000 $
1 550 Normaltown 78.000 $
4 200 Skid Row 150.000 $
3 2000 Hipsterton ????????
Bedrooms Sq. feet Neighborhood Sale price
3 2000 Normaltown 250.000 $
2 800 Hipsterton 300.000 $
2 850 Normaltown 150.000 $
1 550 Normaltown 78.000 $
4 200 Skid Row 150.000 $
3 2000 Hipsterton ????????

13. SUPERVISED LEARNING

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14
STEP 1: PRICE WITH A SIMPLE WEIGHT
price = num_bedrooms * 1 + sqft * 1 + neighborhood * 1

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STEP 2: RUN FUNCTION & CHECK GUESSING VALUES
Bedrooms Sq. feet Neighborhood Sale price Guess
3 2000 Normaltown 250.000 $ 178.000 $
2 800 Hipsterton 300.000 $ 371.000 $
2 850 Normaltown 150.000 $ 148.000 $
1 550 Normaltown 78.000 $ 101.000 $
4 200 Skid Row 150.000 $ 121.000 $

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STEP 3: CHANGE THE WEIGHT VALUES
price = num_bedrooms * 0.8 + sqft * 1.2 + neighborhood * 0.4

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price = num_bedrooms * 0.8 + sqft * 1.2 + neighborhood * 0.4
STEP 3: REPEAT UNTIL YOU TRY ALL THE NUMBERS
Adjust
weight
Calculate
Adjust
weight
Calculate
Adjust
weight
Calculate

18. TRYING ALL THE NUMBER
WOULD BE PRETTY
EXPENSIVE OR IMPOSSIBLE…

19. 19
19
COST FUNCTION
A cost function is something you want to minimize

20. 20
20
COST FUNCTION
A cost function is something you want to minimize
#training examples
#minimize

21. 21
21
GRADIENT DESCENT
How wrong our estimating price function is for the
weights we currently have set

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22
GRADIENT DESCENT
How wrong our estimating price function is for the
weights we currently have set
#current weight

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COST FOR YOUR CURRENT WEIGHTS
Graphical representation for num of bedrooms and sqft

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COST FOR YOUR CURRENT WEIGHTS
Graphical representation for num of bedrooms and sqft
#lowest point

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? MAGIC ?
No, as long as we have enough data to solve the problem
#lowest point

26. ANOTHER APPROACH

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27
DIFFERENT WEIGHT VALUES

28. 28
28
COMBINE PRICE ESTIMATION TO GET FINAL PRICE

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NEURAL NETWORK

30. 30
30
NEURAL NETWORK
#hidden layers

31. Artificial neural networks (ANNs) are computing systems inspired by
the biological neural networks that constitute animal brains. Such
systems learn (progressively improve performance) to do tasks by
considering examples, generally without task-specific programming.
Wikipedia

32. THANK YOU!
@RaduVunvulea
vunvulearadu.blogspot.com
https://www.linkedin.com/in/raduvunvulea/
vunvulear@outlook.com