Week |
主題與課程內容 |
學習投入時數 |
Week 1 |
Introduction |
6 |
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Machine Learning Book |
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A Course in Machine Learning |
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Week 2 |
Overview of Statistical Pattern Recognition |
6 |
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Basic Math |
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Week 3 |
Bayesian Decision Rule |
6 |
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Bayesian Decision Rule: General Case |
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Week 4 |
Multivariate Normal Distribution |
6 |
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Independent Binary Features |
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Week 5 |
Parameter Estimation--Maximum-likelihood and Bayesian Methods |
6 |
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Week 6 |
Parameter Estimation--Maximum-likelihood and Bayesian Methods |
6 |
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PCA |
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Week 7 |
Markov Chains |
6 |
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Hidden Markov Models |
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Week 8 |
Non-Parametric Estimation |
6 |
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Nearest Neighbor Rule |
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Week 9 |
Linear Discriminant Functions |
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Support Vector Machines |
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Gradient Decent |
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Week 10 |
Dimensionality Reduction: FLD, LPP,ICA |
6 |
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Week 11 |
Midterm |
10 |
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Week 12 |
Similarity Measure |
6 |
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Clustering |
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Feature Selection |
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Week 13 |
Artificial Neural Networks (ANN) |
6 |
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Multilayer Neural Networks |
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Week 14 |
Deep Learning: Tutorial |
6 |
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Week 15 |
Convolutional Neural Networks |
6 |
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Recurrent Neural Networks |
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Week 16 |
Deep Learning Toolkits |
6 |
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Week 17 |
AI in Medicine |
6 |
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Drone Intelligence |
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Biometrics |
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Week 18 |
Project Presentation |
12
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