This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.

1. Machine Learning
Aakash Chotrani (MSCS)
Supervised, Unsupervised and Reinforcement Learning

2. Machine Learning
Supervised, Unsupervised and
Reinforcement Learning
A. What is Machine Learning?
B. Applications of Machine Learning
C. Supervised Learning
D. Unsupervised Learning
E. Reinforcement Learning
F. Conclusion

3. Data is the King

4. What is Machine Learning?
GOAL = Building models for predicting value/class of unseen data

5. Solving a general problem
Algorithms are the Central part

6. Solving a Machine learning problem
Algorithms + Data = Central part

7. Machine Learning
Supervised, Unsupervised and
Reinforcement Learning
A. What is Machine Learning?
B. Applications of Machine Learning
C. Supervised Learning
D. Unsupervised Learning
E. Reinforcement Learning
F. Conclusion

8. Application of ML

9. Machine Learning
Supervised, Unsupervised and
Reinforcement Learning
A. What is Machine Learning?
B. Applications of Machine Learning
C. Supervised Learning
D. Unsupervised Learning
E. Reinforcement Learning
F. Conclusion

10. Supervised Learning
Learning from the association between Input and Output
Input:
Output:
1 3 4 7
1 9 16 49
10
?
F(x) = x2
(Function Approximation)

11. Another Practical Example of Supervised Learning
$
Years of experience

12. Data Preprocessing
Training set
Test set
Dataset

13. Plotting training set on graph

14. Equation for straight line(Function Approximation)
y = b0 + b1*x1
Dependent Variable
(salary)
Independent Variable
(Years of experience)

15. Building the model

16. Different Regression models
Polynomial Regression Support vector regression
Decision Tree Regression Random Forest Regression

17. Some Applications of Supervised Learning

18. Machine Learning
Supervised, Unsupervised and
Reinforcement Learning
A. What is Machine Learning?
B. Applications of Machine Learning
C. Supervised Learning
D. Unsupervised Learning
E. Reinforcement Learning
F. Conclusion

19. Unsupervised Learning
Find Interesting things about the data(clusters)

20. Unsupervised Learning
We derive the structure from input by just looking at relation between
Input themselves
Finding structure within the data without labels
Low earning group
High earning group

21. What we try to Achieve
We try to organize something
Example: We have 20 million data items and we want to group them
We make up some sort to criteria to find a cluster
that scores well

22. No correct criteria!

23. Machine Learning
Supervised, Unsupervised and
Reinforcement Learning
A. What is Machine Learning?
B. Applications of Machine Learning
C. Supervised Learning
D. Unsupervised Learning
E. Reinforcement Learning
F. Conclusion

24. Reinforcement Learning
Learning from delayed reward
Agent Environment
State
Action
Reward

25. Example : Tic-Tac-Toe

26. Example: Pacman

27. Reinforcement Learning
• Feedback after several steps
• We try to find the behavior which scores well
• Computation happens within the agent.
• No idea about the environment beforehand
• Learns about the environment through interaction with
the environment

28. Conclusion
Supervised Learning
• Learning through delayed feedback by interacting with environment
Reinforcement Learning
Unsupervised Learning
• We are trying to find association between input values and grouping them
• We are performing function approximation based on input and output values

29. Questions/Comments