This document provides an overview of machine learning, including its history and major methods. It discusses Alan Turing's work on determining if machines can think and pass the Turing Test. Examples are given of projects like Eugene the chatbot and Google Duplex that have passed versions of the test. Supervised, unsupervised, and reinforcement learning techniques are defined at a high level. Applications of machine learning like IBM's Deep Blue chess program and Google's AlphaGo are mentioned. The document concludes that machine learning will continue to grow in importance and be applied to more areas.

1. An Overview of
Machine Learning
Oswin Patrick Rozario
19ESKCS165
2022/09/25

2. IDEA TO REALITY

3.  What is machine learning?
 History of Machine Learning
 ML Methods
 Supervised Learning
 Unsupervised Learning
 Reinforcement Learning
 Advantages of ML
 Disadvantages of ML
 Use-Cases of ML
 Applications of ML
 Some Famous Personalities
 Conclusion
Outline & Content

4. What is machine learning?

7. History Of Machine Learning

8.  Alan Turing wanted to answer the
question, CAN MACHINES THINK ?
What is Turing test?

9. The Turing Test

10.  A chatbot named Eugene Goostman is
accepted by some as the first to pass the
Turing Test, in 2014.
 In 2018, Google Duplex was introduced at
the annual Google I/O Developer
Conference. The machine scheduled a
hair salon appointment and interacted with
a hair salon assistant via the phone as
part of the conversation.
Fun Facts About Turing Test!

11.  Deep Blue was a supercomputer developed by
IBM specifically for playing chess and was best
known for being the first artificial intelligence
construct to ever win a chess match against a
reigning world champion, Grandmaster Garry
Kasparov, under regular time controls.
 In its final configuration, the computer used 256
processors, with an ability to evaluate 200
million chess positions per second.
IBM’s Deep Blue

12. What Actually Happened?

13. 10^20
AlphaGO
10^40
10^80

14. ML METHODS

15. 15
ML Methods Visualization-
Supervised
learning
Unsupervised
learning
Semi-supervised

16. Supervised Machine Learning

17. Supervised Machine Learning

18. Unsupervised ML-

19. Unsupervised ML-

20. Reinforcement ML-

25. ML APPLICATIONS-

26.  1. Andrej Karpathy
Some Famous Personalities

28. We have a simple overview of some
techniques and algorithms in machine
learning. Furthermore, there are more and
more techniques apply machine learning as
a solution. In the future, machine learning
will play an important role in our daily life.
Conclusion