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Course Outline
Math Formula Reference
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1.1 Introduction to Machine Learning and Artificial Intelligence
1.2 Review of Statistics and Bayes Theorem
1.3 Introduction to Pattern Recognition
1.4 Optimal Bayes Classifier and Principal Component Analysis
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2.1 Introduction to Machine Learning
2.2 Agent Performance Evaluation
2.3 Introduction to Machine Vision and Image Processing
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3.1 Image Texture and Filter Kernels
3.2 Data Representation: Bag of Visual Words
3.3 Manual Image Features
3.4 Random Sample Consensus (Optional)
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4.1 Deep Learning Applications
4.2 Introduction to Neural Networks
4.3 Convolutional Neural Networks
4.4 Recurrent Neural Networks and Long-Short Term Memory Cells
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5.1 Generative Adversarial Networks
5.2 Reinforcement Learning
Introduction to Artificial Intelligence