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.
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
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?
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).
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.
13.
14. Alan Turing
Alan Turing described the
“universal computing
machine,” a “single machine
that can be used to compute
any computable sequence.”
(Turing, 1936)
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.
19.
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.
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.
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.
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
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.
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.
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
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