AI Foundations

1. Artificial Intelligence Foundations: • Machine Learning, Deep Learning • and Neural Networks www.leewayhertz.com

2. Contents: • Overview • Machine Learning • History of ML • Deep Learning • History of DL • Neural Networks • History of NN www.leewayhertz.com

3. Overview • Artificial Intelligence system consists of: • People • Procedures • Hardware • Software • Data • And Knowledge Needed to develop computer systems and machines that demonstrate the characteristics of intelligence. www.leewayhertz.com

4. Machine Learning Machine Learning Output Optimum Model Input Data Information (+ Answers) • Relationships • Patterns • Dependencies • Hidden Structures www.leewayhertz.com

5. Training Phase Machine Learning Algorithm Labels Feature Extractor Images Features Prediction Phase Feature Extractor Trained Classifier Images Features Label Machine Learning Phases Traditional ML Algorithm Feature Extractor Image Features Output Traditional Machine Learning Flow www.leewayhertz.com

6. History of ML + Decision Trees Quinlan, 1979 (ID3) Breiman, 1984 (CART) Ensembles Breiman, 1994 (Bagging) Breiman, 2001 (Random Forests) Boosting Schapire, 1989 (Boosting) Schapire, 1995 (Adaboost) Interpretability Support Vector Machines Vapnik, 1963 (ANN) Corina & Vapnik, 1995 Neural Networks Minsky, 1969 Deep Learning Fukushima, 1989 (ANN) Hinton, 2006 Perception Rosenblatt, 1957 - 1950 1960 1970 1980 1990 2000 2010 2020 www.leewayhertz.com

7. Deep Learning Deep Learning is a set of algorithms in machine learning that attempts to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. Image Deep Learning Flow Output Deep Learning Flow www.leewayhertz.com

8. History of DL McCulloch/Pitts Neurons Hebb's Organization of Behavior Rosenblatt's Perceptions Multi-Layer Perceptrons Backpropagation Hopfield Networks Convolutional Neural Networks Long Short-Term Memory (LSTM) Deep Learning Deep Learning with GPUs 1940 1950 1960 1970 1980 1990 2000 2010 www.leewayhertz.com

9. Neural Networks A Neural Network is a system composed of many simple processing elements operating in parallel which can acquire, store, and utilize experimental knowledge www.leewayhertz.com

10. Preprocessing Raw Data Grayscale Image Size Train RBM Data Set Extracted Features Features Map Reshape Extracted Features Output Train CNN Neural Network Flow www.leewayhertz.com

11. History of Neural Networks LVQ, SOM 1981, 1982 RBF 1988 Continuous Hopfield 1984 Discrete Hopfield 1982 Boltzmann Machine 1984 Modified Backpropagating Perception 1986-1990 Backpropagating Perception 1974 Perception 1958 Perception 1958 1950 1960 1970 1980 1985 1990 www.leewayhertz.com

12. Want to know more about • AI, ML, and DL? www.leewayhertz.com

13. Thank You! www.leewayhertz.com