FUNDAMENTALS OF MACHINE LEARNING AND DEEP LEARNING An introduction Presented by Dr. Stephen Gbenga Fashoto. Department of Computer Science, Faculty of Science & Engineering University of Swaziland.
Machine Learning Supporting Disciplines
Fundamentals of Machine Learning Developed from Artificial Intelligence The first artificial neuron learning is the perceptron by Frank Rosenblatt 1957 Key Elements of Machine Learning Representation: how to represent knowledge. Evaluation: the way to evaluate performance using metrics. Optimization: it is used to determine the optimal result. Historical Perspective Supervised Machine Learning Unsupervised Machine Learning Semi - supervised Machine Learning Reinforcement Machine Learning Deep Learning
Supervised Machine Learning What is SML Goal of SML Classification of SML Problems Examples of SML Algorithm are : Linear regression for regression problems Neural networks for classification and regression problems. Random forest for classification and regression problems. Support vector machines for classification problems. Regression 3D Classification Reflection Reflection
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Unsupervised Machine Learning What is UsML Goal of UsML Classification of UsML Problems Examples of UsML Algorithm are : k-means for clustering problems. Apriori algorithm for association rule learning problems. Clustering 3D Association Reflection Reflection
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Semi supervised Machine Learning Clustering What is S-SML Goal of S-SML 3D Reflection Association Reflection
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Reinforcement Learning Regression What is RL Goal of RL Steps in RL Examples of RL Algorithm are : Q-Learning Temporal Difference (TD) Deep Adversarial Networks 3D Reflection Reflection Classification
Deep Learning Algorithms Regression What is DL Goal of DL Steps in DL Examples of DL Algorithm are : Deep Boltzmann Machine (DBM) Deep Belief Networks (DBN) Convolutional Neural Network (CNN) Stacked Auto-Encoders Recurrent Neural Network(RNN) 3D Reflection Reflection Classification
Convolutional Neural Network (CNN) Regression Introduction to CNN Application of CNN 3D Reflection Reflection Classification
Performance of Machine Learning Under – fitting Graph Clustering 3D Reflection Association Reflection
Over – fitting Graph Clustering 3D Reflection Association Reflection
Generalization Graph Clustering 3D Reflection Association Reflection
Comparison of Machine Learning and Deep Learning Data dependencies Hardware dependencies Execution time
PRACTICAL SESSION ON WEKA 3.8.2 DEMO
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