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- 2. A Bit AboutMe…. 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
- 3. Learning Objectives Describethe Information Processing Cycle Understand Input, Data and DataStructures 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
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- 10. In computing, aninput device is a peripheral device used to provide data and control signals to an information processing system
- 11. Trivia Question WhoIs known as the creator of modern computing? In the 1930’s, he described the “universal computing machine”.
- 12. Trivia Question WhoIs 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 Turingdescribed the “universal computing machine,” a “single machine that can be used to compute any computable sequence.” (Turing, 1936)
- 15. Trivia Question Whois one of the pioneers in Artificial Intelligence?
- 16. Trivia Question Whois one of the pioneers in Artificial Intelligence? Who was the first to illustrate machine learning.
- 17. Trivia Question Whois 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 Whois 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 ThroughTree of Possible Moves
- 22. Trivia Question WhoIs known for illustrating artificial neural networks?
- 23. Trivia Question WhoIs known for illustrating artificial neural networks? He created Perceptron
- 24. Trivia Question WhoIs known for illustrating artificial neural networks? He created Perceptron His initials are F.R.
- 25. In The Beginning… CreatorofModern ComputingThe Game of Checkers – Machine Learning Perceptron– Artificial Neural Network
- 26. Frank Rosenblatt’s Perceptron FrankRosenblattcreatedthe Perceptronin 1957which was a first artificial neural network.
- 27. A Few MachineLearning Algorithms Decision Tree Learning Association Rule Learning Artificial Neural Networks
- 28. Decision Tree Learning DecisionTree Learning
- 29. Decision Tree Learning DecisionTree 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 DecisionTree 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 AssociationRule Learning
- 32. Association Rule Learning AssociationRule Learning A method for discovering interesting relations between variables in large databases.
- 33. Association Rule Learning AssociationRule Learning A method for discovering interesting relations between variables in large databases.
- 34. Association Rule Learning AssociationRule Learning A method for discovering interesting relations between variables in large databases.
- 35. Artificial Neural Networks ArtificialNeural Networks
- 36. Artificial Neural Networks ArtificialNeural 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 ArtificialNeuralNetworks 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 ArtificialNeuralNetworks 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, etal 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.
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- 41. EMYCIN Expert System ( Van Melle,Shortliffe & Buchanan, 1981)
- 42. Current Machine LearningLanguages MATLAB/Octave R Python Java Family C Family
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- 44. Supervised Learning: PredictiveModel Feature Supervised Learning Strategy Use a predictive model that is given clear instructions
- 45. Supervised Learning: PredictiveModel Feature Supervised Learning Strategy Use a predictive model that is given clear instructions
- 46. Feature Supervised Learning Strategy Usea predictive model that is given clear instructions Algorithm Nearest neighbor, Naïve Bayes, Decision Trees,Regression Supervised Learning: Predictive Model
- 47. Feature Supervised Learning Strategy Usea predictive model that is given clear instructions Algorithm Nearest neighbor, Naïve Bayes, Decision Trees,Regression Supervised Learning: Predictive Model
- 48. Feature Supervised Learning Strategy Usea predictive model that is given clear instructions Algorithm Nearest neighbor, Naïve Bayes, Decision Trees,Regression Supervised Learning: Predictive Model
- 49. Feature Supervised Learning Strategy Usea 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 Usea 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 Usea 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 Usea 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 Usea 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: DescriptiveModel Feature Unsupervised Learning Strategy Uses a descriptive model whereno target is set and no single featureis more importantthan the other.
- 55. Unsupervised Learning: DescriptiveModel Feature Unsupervised Learning Strategy Uses a descriptive model whereno target is set and no single featureis more importantthan the other.
- 56. Feature Unsupervised Learning StrategyUses 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 StrategyUses 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 StrategyUses 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 StrategyUses 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 StrategyTrains 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 StrategyTrains 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 StrategyTrains 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 StrategyTrains 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 StrategyTrains 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 StrategyTrains 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 thecomputer machine/program must do some work after it is given data. (Brand,2015)
- 67. Azure Machine LearningWorkflow (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 FinancialProviders 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 HealthcareProviders 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 canuse Machine Learning to determine fast and slow moving products.
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- 75. In Conclusion… MachineLearning is a subset of Artificial Intelligence.
- 76. In Conclusion… MachineLearning 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… MachineLearning 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.
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- 80. Summary We offereda brief history and definition of Machine Learning
- 81. Summary We offereda brief history and definition of Machine Learning We explored different types and applications of Machine Learning
- 82. Summary We offereda 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 areyour thoughts, ideas, suggestions on Machine Learning?