This document provides an overview of machine learning including: definitions of machine learning; types of machine learning such as supervised learning, unsupervised learning, and reinforcement learning; applications of machine learning such as predictive modeling, computer vision, and self-driving cars; and current trends and careers in machine learning. The document also briefly profiles the history and pioneers of machine learning and artificial intelligence.

1. Machine Learning
Anastasia Rashtchian

2. A Bit About Me….
Computer Consultant, Trainer and Educator
Master of Science – Computer Science
 Artificial Intelligence and Expert Systems
 Interest: Automated Adaptive Learning Systems
Master of Education – Education, Policy, Organization and
Leadership
 eLearning in Higher Education
 Interest: Online Knowledge Communities

3. Learning Objectives
 What is Machine Learning?

4. Learning Objectives
 What is Machine Learning?
 What are some algorithms, types, and languages of Machine Learning?

5. Learning Objectives
 What is Machine Learning?
 What are some algorithms, types, and languages of Machine Learning?
 What are some applications for Machine Learning?

6. Learning Objectives
 What is Machine Learning?
 What are some algorithms, types, and languages of Machine Learning?
 What are some applications for Machine Learning?
 What are some current trends and research in Machine Learning?

7. Learning Objectives
 What is Machine Learning?
 What are some algorithms, types, and languages of Machine Learning?
 What are some applications for Machine Learning?
 What are some current trends and research in Machine Learning?
 What are some careers with Machine Learning skills?

8. Discussion Question
 What does machine learning mean to you?

9. What is Machine Learning?
Machine learning is the
subfield of computer
science where
computers “are given
the ability to learn
without being explicitly
programmed” (Samuel,
1959).

10. Trivia Question
 Who Is known as the creator of modern computing?

11. Trivia Question
 Who Is known as the creator of modern computing?
 In the 1930’s, he described the “universal computing machine”.

12. Trivia Question
 Who Is known as the creator of modern computing?
 In the 1930’s, he described the “universal computing machine”.
 His initials are A.T.

14. Alan Turing
Alan Turing described the
“universal computing
machine,” a “single machine
that can be used to compute
any computable sequence.”
(Turing, 1936)

15. Trivia Question
 Who is one of the pioneers in Artificial Intelligence?

16. Trivia Question
 Who is one of the pioneers in Artificial Intelligence?
 Who was the first to illustrate machine learning.

17. Trivia Question
 Who is one of the pioneers in Artificial Intelligence?
 Who was the first to illustrate machine learning.
 His checkers-playing program was the world's first self-learning program.

18. Trivia Question
 Who is one of the pioneers in Artificial Intelligence?
 Was the first to illustrate machine learning.
 His checkers-playing program was the world's first self-learning program.
 His initials are A.S.

20. Arthur Samuel’s
Gameof Checkers
 Arthur Samuel (1901–1990) was a
pioneer of artificial intelligence
research and was the first to
illustrate the concept of machine
learning in his Game of Checkers.
 His Checkers-playing Program
(Samuels, 1959) appears to be the
world's first self-learning program.

21. Look Ahead Through Tree of Possible Moves

22. Trivia Question
 Who Is known for illustrating artificial neural networks?

23. Trivia Question
 Who Is known for illustrating artificial neural networks?
 He created Perceptron

24. Trivia Question
 Who Is known for illustrating artificial neural networks?
 He created Perceptron
 His initials are F.R.

25. In The Beginning…
Creatorof Modern
ComputingThe Game of
Checkers –
Machine
Learning
Perceptron–
Artificial
Neural
Network

26. Frank Rosenblatt’s
Perceptron
 Frank Rosenblattcreatedthe
Perceptronin 1957which was a
first artificial neural network.

27. A Few Machine Learning Algorithms
 Decision Tree Learning
 Association Rule Learning
 Artificial Neural Networks

28. Decision Tree Learning
Decision Tree Learning

29. Decision Tree Learning
Decision Tree Learning
Uses a decision tree as
a predictive model, which
maps observations about
an item to conclusions
about the item's target
value.

30. Decision Tree Learning
Decision Tree Learning
Uses a decision tree as
a predictive model, which
maps observations about
an item to conclusions
about the item's target
value.

31. Association Rule Learning
Association Rule Learning

32. Association Rule Learning
Association Rule Learning
A method for discovering
interesting relations
between variables in large
databases.

33. Association Rule Learning
Association Rule Learning
A method for discovering
interesting relations
between variables in large
databases.

34. Association Rule Learning
Association Rule Learning
A method for discovering
interesting relations
between variables in large
databases.

35. Artificial Neural Networks
Artificial Neural Networks

36. Artificial Neural Networks
Artificial Neural Networks
Computations are
structured in terms of an
interconnected group
of artificial neurons,
processing information
using
a connectionist approach
to computation.

37. Artificial Neural Networks
Artificial NeuralNetworks
Computations are structured
in terms of an interconnected
group of artificial neurons,
processing information using
a connectionist approach
to computation.
Modern neural networks
are non-linear statisticaldata
modeling tools.

38. Artificial Neural Networks
Artificial NeuralNetworks
Computations are structured
in terms of an interconnected
group of artificial neurons,
processing information using
a connectionist approach
to computation.
Modern neural networks
are non-linear statisticaldata
modeling tools.

39. Lisp, Prolog, et al
 Lisp created by John McCarthy in 1958
 Prolog created by Alain Colmerauer and Philippe Roussel in 1972
 Allows for the logic programming needed for traversal creation of the
neural networks
 Recognizes the relationships between the data and their rules.
 Semantic nets represent knowledge in tree-like patterns connecting
nodes and arcs based on these rules.

40. Semantic
Neural
Network

41. EMYCIN
Expert
System
( Van Melle, Shortliffe & Buchanan, 1981)

42. Current Machine Learning Languages
 MATLAB/Octave
 R
 Python
 Java Family
 C Family

43. (Computer Vision, 2015)

44. Supervised Learning: Predictive Model
Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions

45. Supervised Learning: Predictive Model
Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions

46. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Supervised Learning: Predictive Model

47. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Supervised Learning: Predictive Model

48. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Supervised Learning: Predictive Model

49. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Supervised Learning: Predictive Model
Decision Tree Classification

50. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Supervised Learning: Predictive Model
Decision Tree Classification

51. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Use Predict the likelihood
of an earthquakeor
tornado
Supervised Learning: Predictive Model

52. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Use Predict the likelihood
of an earthquakeor
tornado
Supervised Learning: Predictive Model

53. Feature Supervised
Learning
Strategy Use a predictive
model that is given
clear instructions
Algorithm Nearest neighbor,
Naïve Bayes, Decision
Trees,Regression
Use Predict the likelihood
of an earthquakeor
tornado
Supervised Learning: Predictive Model

54. Unsupervised Learning: Descriptive Model
Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.

55. Unsupervised Learning: Descriptive Model
Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.

56. Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.
Algorithm K-meansClustering
Algorithm
Unsupervised Learning: Descriptive Model

57. Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.
Algorithm K-meansClustering
Algorithm
Unsupervised Learning: Descriptive Model

58. Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.
Algorithm K-meansClustering
Algorithm
Use Predict which diseases are
likely to occuralong with
diabetes.
Unsupervised Learning: Descriptive Model

59. Feature Unsupervised Learning
Strategy Uses a descriptive model
whereno target is set and
no single featureis more
importantthan the other.
Algorithm K-meansClustering
Algorithm
Use Predict which diseases are
likely to occuralong with
diabetes.
Unsupervised Learning: Descriptive Model

60. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Reinforcement Learning

61. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Reinforcement Learning

62. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Algorithm Markov Decision Process
Reinforcement Learning

63. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Algorithm Markov Decision Process
Reinforcement Learning

64. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Algorithm Markov Decision Process
Use Self driving carsuse it to make
decisionscontinuously on
which routeto take and what
speed to driveand so on…
Reinforcement Learning

65. Feature Reinforcement Learning
Strategy Trains itself on a continualbasis
based on the environmentit is
exposed to,and applies it’s
enriched knowledgeto solve
problems.
Algorithm Markov Decision Process
Use Self driving carsuse it to make
decisionscontinuously on
which routeto take and what
speed to driveand so on…
Reinforcement Learning

66. Machine
Learning
Work Flow
"Machine
Learning"
emphasizes that
the computer
machine/program
must do some
work after it is
given data.
(Brand,2015)

67. Azure Machine Learning Workflow
(Grondlund,2016)

68. Google and Facebook
 Google and Facebook
use Machine Learning
extensively to push
their respective ads to
the relevant users.

69. Banking and Financial Providers
 Banking and Financial
Providers can use
Machine Learning to
predict the customers
who are likely to
default from paying
loans or credit card
bills.

70. Healthcare Providers
 Healthcare Providers
can use Machine
Learning to diagnose
deadly diseases based
on the symptoms of
patients and tallying
them with the past
data of similar kind of
patients.

71. Retailers
 Retailers can use
Machine Learning to
determine fast and
slow moving products.

72. Artificial
Intelligence
Technological
Advances
and Trends

73. Machine
Intelligence
Trends and
Applications

74. Careers
with
Machine
Learning
Skills

75. In Conclusion…
 Machine Learning is a subset of Artificial Intelligence.

76. In Conclusion…
 Machine Learning is a subset of Artificial Intelligence.
 It refers to the techniques involved in dealing with vast data, in the
most intelligent fashion, (by developing algorithms) to derive
actionable insights.

77. In Conclusion…
 Machine Learning is a subset of Artificial Intelligence.
 It refers to the techniques involved in dealing with vast data, in the
most intelligent fashion, (by developing algorithms) to derive
actionable insights.
 There are a wide variety of algorithms and techniques to aid in
machine learning and the technique chosen is determined by what
one wants the machine to learn.

78. Python
Implementations
of Machine
Learning
Algorithms
https://github.com/rushter/MLAlgorithms

79. Machine Learning Refined
http://mlrefined.wixsite.com/home-page

80. Summary
 We offered a brief history and definition of Machine Learning

81. Summary
 We offered a brief history and definition of Machine Learning
 We explored different types and applications of Machine Learning

82. Summary
 We offered a brief history and definition of Machine Learning
 We explored different types and applications of Machine Learning
 We looked at current trends, research and careers in Machine
Learning.

83. References
Blank, S. (2014) Tools and Blogs for Entrepreneur.Retrievedfromhttps://steveblank.com/tools-and-blogs-for-
entrepreneurs/.
Chen, F. (2016). AI, Deep Learning, and Machine Learning a Prime. Retrieved fromhttp://a16z.com/2016/06/10/ai-deep-
learning-machines/.
Computer Visions.(2015). Deep Leariningverus Machine Learning. Retrieved from
http://www.computervisionblog.com/2015/03/deep-learning-vs-machine-learning-vs.html.
Grondlund,C.J..(2016). Introductionto machine learningin the cloud.Retrieved from https://docs.microsoft.com/en-
us/azure/machine-learning/machine-learning-what-is-machine-learning.
Khan, M. (2016). Minimal and clean Python implementations ofMachine Learningalgorithms.Great for learninghow
these algorithms work! Retrieved from https://www.linkedin.com/groups/2642596/2642596-6204217888639934466
McCarthy, J. & Feigenbaum,E.(1990). In Memoriam ArthurSamuel: Pioneer in Machine Learning. AI Magazine.
AAAI.11 (3). Retrieved fromhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/840/758.
Nvidia.(2016). What’s the Difference Between Artificial Intelligence, Machine Learning,and Deep Learning? Retrieved
from https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-
ai/.
Vidya.(2016). Machine LearningBasics. Retrieved from https://www.analyticsvidhya.com/blog/2015/06/machine-
learning-basics/.

84. Questions?
 What are your thoughts, ideas, suggestions on Machine Learning?