2. Machine Learning for dotNET Developer Bahrudin Hrnjica, MVP

3. Agenda Intro to ML Types of ML dotNET and ML-tools and libraries
Demo01: ANN with C#
Demo02: GP with C#
.NET Tools – Acord.NET, GPdotNET
Summary

4. Machine Learning?
method of teaching computers to make predictions based on data.
branch of Artificial intelligence
semi-automated extraction of knowledge from data
always starts from data, and the goal is knowledge extraction,
involves some amount of automation in form of algorithm and computer to do the job,
not fully automated, it requires many smart decisions by a human.

5. Data Computer Output Program Data Computer Program Output
Traditional Programming
Machine Learning
Computer
Data
Output
Program
Computer
Data
Program
Output

6. Magic? No, more like gardening Seeds = Algorithms Nutrients = Data
Gardener = You
Plants = Programs

7. Sample Applications Web search Engineering Finance E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
[Your favorite area]

8. ML in a Nutshell Tens of thousands of machine learning algorithms
Hundreds new every year
Every machine learning algorithm has three components:
Representation
Evaluation
Optimization

9. Representation Chromosomes in genetics (GA/GP) Neural networks
Decision trees
Sets of rules / Logic programs
Instances
Graphical models (Bayes/Markov nets)
Support vector machines
Etc.

10. Evaluation Accuracy Precision and recall Squared error Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.

11. Optimization/Learners
Combinatorial optimization
E.g.: Greedy search
Convex optimization
E.g.: Gradient descent
Constrained optimization
E.g.: Linear programming
General optimization
E.g.: Genetic Algorithm
E.g.: Particle Swarm Optimization

12. Types of Learning Supervised (inductive) learning
Training data includes desired outputs
Unsupervised learning
Training data does not include desired outputs
Semi-supervised learning
Training data includes a few desired outputs

13. Machine learning structure
Supervised learning

14. Machine learning structure
Unsupervised learning

15. Training and testing Data acquisition Practical usage Universal set
(unobserved)
Training set (observed)
Testing set
(unobserved)

16. Training and testing
Training is the process of making the system able to learn.
No free lunch rule:
Training set and testing set come from the same distribution
Need to make some assumptions or bias

17. What are we seeking?
Supervised: Low E-out or maximize probabilistic terms
Unsupervised: Minimum quantization error, Minimum distance, MAP, MLE(maximum likelihood estimation)
E-in: for training set
E-out: for testing set

18. What are we seeking? Under-fitting VS. Over-fitting (fixed N) error
(model = hypothesis + loss functions)

19. dotNET and ML
Learning API -
Accord .NET -
GPdotNET-

20. DEMO- Simple ANN and GP C# Program
IRIS DATA DEMO

21. Accord .NET

22. DEMO
ACCORD .NET

23. LearningAPI

24. GPdotNET
GP and ANN

25. Existing ML tools are difficult or impossible to integrate into a software system.
Commercial and Open Source API libraries work well for some machine learning tasks but are extremely limited for neural networks.
To develop neural networks using Visual Studio you must understand seven core concepts: feed- forward, activation, data encoding, error, training, free parameters, and over-fitting.
Once the concepts are mastered, implementation with Visual Studio is not difficult (but not easy either).

26. Reference http://bhrnjica.net/gpdotnet http://accord-framework.net/pi
C# ANN sample
us/magazine/mt149362?author=james+mccaffrey

27. HVALA NA PAŽNJI!