Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1pWkcUI. Alena Hall presents various machine learning algorithms available in the Accord.NET - a framework for machine learning and scientific computing in .NET. Hall also takes a look at sample types of problems to see how we can apply machine learning algorithms using the Accord.NET framework with F# functional approach. Filmed at qconsf.com. Alena Hall is a young researcher in the field of theoretical mathematical abstractions and innovative algorithmic models possible in modern programming concepts. She is a member of F# Software Foundation Board of Trustees. Alena currently works as a Software Architect and has more than 10 years of professional experience including complex distributed systems and cloud computing.

1. m
Machine Learning
F# and Accord.net

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4. Alena Hall
• Software architect, MS in Computer Science
• Member of F# Software Foundation Board
of Trustees
• Researcher in the field of mathematical
theoretical abstractions possible in modern
programming concepts
• Speaker and active software engineering
community member
@lenadroid

5. Machine
Learning

6. • Why machine learning?
• What is the data?
• How?
Questions

7. Data Questions.

8. Data reality :

9. Path to grasping
machine learning
and data science…

10. Contents
• Multiple Linear  Regression
• Logistic Regression  Classification
• K Means  Clustering
• What’s next?

11. F# for machine learning
and data science!

12. Why F#?
1. Exploratory programming, interactive environment
2. Functional programming, referential transparency
3. Data pipelines
4. Algebraic data types and pattern matching
5. Strong typing, type inference,Type Providers
6. Units of measure
7. Concurrent, distributed and cloud programming

13. Data pipelines

14. Algebraic data types
// Discriminated Union

15. Pattern matching

16. Type Providers

17. Units of measure

19. Linear Regression

20. How to predict?
1. Make a guess.
2. Measure how wrong the guess is.
3. Fix the error.

21. Make a guess!

22. MATH

23. Make a guess?
What does it mean?...
Hypothesis /guess :
weights

24. Find out our mistake…
Cost function/ Mistake function:
… and minimize it:

25. Mistake function looks like…
Global minimums

26. How to reduce the mistake?
Update each slope parameter until Mistake Function
minimum is reached:
Simultaneously
Alpha
Learning rate
Derivative
Direction of moving

27. Fix the error

28. Multiple Linear Regression
X [ ] – Predictors:
Statistical data about bike rentals for previous years or
months.
Y – Output:
Amount of bike rentals we should expect today or some
other day in the future.
*Y is not nominal, here it’s numerical continuous range.

29. Make a guess!

30. Fix the error

31. Multiple linear regression: Bike rentals demand
“Talk is cheap. Show me the code.”

32. What to remember?
1. Simplest regression algorithm
2. Very fast, runs in constant time
3. Good at numerical data with lots of features
4. Output from numerical continuous range
5. Linear hypothesis
6. Uses gradient descent
Linear Regression

33. Logistic Regression

34. Hypothesis function
Estimated probability
thatY = 1 on input X

35. Mistake function
Mistake function is the cost for
a single training data example
h(x)

36. Full mistake function
1. Uses the principle of maximum likelihood estimation.
2.We minimize it same way as with Linear Regression

37. “Talk is cheap. Show me the code.”
Logistic Regression Classification Example

38. What to remember?
• Classification algorithm
• Relatively small number of predictors
• Uses logistics function for hypothesis
• Has the cost function that is convex
• Uses gradient descent for correcting the mistake
Logistic Regression

39. At this point…

40. Machine Learning
What society thinks I do… What other programmers
think I do…

41. What I really do is…

42. K-Means

44. Clustering

46. What’s next?

48. I’m Lena
@lenadroid

49. Thank you!

50. What if it doesn’t
work?

51. • Try more data
• Try more features
• Try less features
• Try feature combinations
• Try polynomial features
• …
Algorithm debugging tips

52. What else can go
wrong?

53. Ideally... the hypothesis will
… just fit the model

54. Underfitting … Overfitting

55. • Regularization…?
• Too big regularization parameter?
-> underfitting - the line is over-smoothed
• Too small regularization parameter?
-> overfitting - too optimized for train data
Try out different values for the
regularization parameter.

56. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/accord-
net-machine-learning