Embed Size (px)
Citation preview
Binary Search Algorithm
Anastasia Rashtchian
A Bit About Me….
Computer Consultant, Trainer and Educator
Master of Science – Computer Science
Illinois Institute of Technology
Artificial Intelligence and Expert Systems
Current Passion: Machine Learning
Master of Education – Education, Policy, Organization Leadership
University of Illinois – Urbana-Champaign
eLearning in Higher Education
Current Passion: Automated Adaptive Learning Systems
Learning Objectives
Describe the Information Processing Cycle
Understand Input, Data and Data Structures
Analyze Sorting and Searching Algorithms
Evaluation the Binary Search Algorithm
Apply the Binary Search Algorithm
Analyze the Benefits and Limitations of the Binary Search Algorithm
The Information Processing Cycle
The Information Processing Cycle
The Information Processing Cycle
The Information Processing Cycle
The Information Processing Cycle
What is Input?
In computing, an input device
is a peripheral device used to
provide data and control
signals to an information processing system
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?
