Machine Learning

Anastasia Rashtchian

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

Learning Objectives

What is Machine Learning?

Learning Objectives

What is Machine Learning?

What are some algorithms, types, and languages of Machine Learning?

Learning Objectives

What is Machine Learning?

What are some algorithms, types, and languages of Machine Learning?

What are some applications for Machine Learning?

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?

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?

Discussion Question

What does machine learning mean to you?

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).

Trivia Question

Who Is known as the creator of modern computing?

Trivia Question

Who Is known as the creator of modern computing?

In the 1930’s, he described the “universal computing machine”.

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.

Alan Turing

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

Trivia Question

Who is one of the pioneers in Artificial Intelligence?

Trivia Question

Who is one of the pioneers in Artificial Intelligence?

Who was the first to illustrate machine learning.

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.

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.

Arthur Samuel’s Game of 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.

Look Ahead Through Tree of Possible Moves

Trivia Question

Who Is known for illustrating artificial neural networks?

Trivia Question

Who Is known for illustrating artificial neural networks?

He created Perceptron

Trivia Question

Who Is known for illustrating artificial neural networks?

He created Perceptron

His initials are F.R.

In The Beginning…

Creator of Modern ComputingThe Game of

Checkers –Machine Learning

Perceptron –Artificial Neural Network

Frank Rosenblatt’s Perceptron

Frank Rosenblatt created the Perceptron in 1957 which was a first artificial neural network.

A Few Machine Learning Algorithms

Decision Tree Learning

Association Rule Learning

Artificial Neural Networks

Decision Tree Learning

Decision Tree Learning

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.

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.

Association Rule Learning

Association Rule Learning

Association Rule Learning

Association Rule Learning

A method for discovering interesting relations between variables in large databases.

Association Rule Learning

Association Rule Learning

A method for discovering interesting relations between variables in large databases.

Association Rule Learning

Association Rule Learning

A method for discovering interesting relations between variables in large databases.

Artificial Neural Networks

Artificial Neural Networks

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.

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.

Modern neural networks are non-linear statistical data modeling tools.

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.

Modern neural networks are non-linear statistical data modeling tools.

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.

Semantic Neural

Network

EMYCINExpert

System

( Van Melle, Shortliffe & Buchanan, 1981)

Current Machine Learning Languages

MATLAB/Octave

R

Python

Java Family

C Family

(Computer Vision, 2015)

Supervised Learning: Predictive Model

Feature Supervised Learning

Strategy Use a predictive model that is given clear instructions

Supervised Learning: Predictive Model

Feature Supervised Learning

Strategy Use a predictive model that is given clear instructions

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

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

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

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

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

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 earthquake or tornado

Supervised Learning: Predictive Model

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 earthquake or tornado

Supervised Learning: Predictive Model

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 earthquake or tornado

Supervised Learning: Predictive Model

Unsupervised Learning: Descriptive Model

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Unsupervised Learning: Descriptive Model

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Algorithm K-means Clustering Algorithm

Unsupervised Learning: Descriptive Model

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Algorithm K-means Clustering Algorithm

Unsupervised Learning: Descriptive Model

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Algorithm K-means Clustering Algorithm

Use Predict which diseases are likely to occur along with diabetes.

Unsupervised Learning: Descriptive Model

Feature Unsupervised Learning

Strategy Uses a descriptive model where no target is set and no single feature is more important than the other.

Algorithm K-means Clustering Algorithm

Use Predict which diseases are likely to occur along with diabetes.

Unsupervised Learning: Descriptive Model

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Reinforcement Learning

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Reinforcement Learning

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Algorithm Markov Decision Process

Reinforcement Learning

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Algorithm Markov Decision Process

Reinforcement Learning

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Algorithm Markov Decision Process

Use Self driving cars use it to make decisions continuously on which route to take and what speed to drive and so on…

Reinforcement Learning

Feature Reinforcement Learning

Strategy Trains itself on a continual basis based on the environment it is exposed to, and applies it’s enriched knowledge to solve problems.

Algorithm Markov Decision Process

Use Self driving cars use it to make decisions continuously on which route to take and what speed to drive and so on…

Reinforcement Learning

Machine Learning Work Flow

"Machine Learning" emphasizes that the computer machine/program must do some work after it is given data.

(Brand, 2015)

Azure Machine Learning Workflow

(Grondlund, 2016)

Google and Facebook

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

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.

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.

Retailers

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

Artificial Intelligence Technological Advances and Trends

Machine Intelligence Trends and Applications

Careers with Machine Learning Skills

In Conclusion…

Machine Learning is a subset of Artificial Intelligence.

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.

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.

Python Implementations of Machine Learning Algorithms

https://github.com/rushter/MLAlgorithms

Machine Learning Refined

http://mlrefined.wixsite.com/home-page

Summary

We offered a brief history and definition of Machine Learning

Summary

We offered a brief history and definition of Machine Learning

We explored different types and applications of Machine Learning

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.

ReferencesBlank, S. (2014) Tools and Blogs for Entrepreneur. Retrieved from https://steveblank.com/tools-and-blogs-for-entrepreneurs/.

Chen, F. (2016). AI, Deep Learning, and Machine Learning a Prime. Retrieved from http://a16z.com/2016/06/10/ai-deep-learning-machines/.

Computer Visions. (2015). Deep Learining verus Machine Learning. Retrieved from http://www.computervisionblog.com/2015/03/deep-learning-vs-machine-learning-vs.html.

Grondlund, C.J.. (2016). Introduction to machine learning in 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 of Machine Learning algorithms. Great for learning how these algorithms work! Retrieved from https://www.linkedin.com/groups/2642596/2642596-6204217888639934466

McCarthy, J. & Feigenbaum, E. (1990). In Memoriam Arthur Samuel: Pioneer in Machine Learning. AI Magazine. AAAI. 11 (3). Retrieved from http://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 Learning Basics. Retrieved from https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/.

Questions?

What are your thoughts, ideas, suggestions on Machine Learning?