lecture_notes
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* Weeks 1-2: A. {{: | * Weeks 1-2: A. {{: | ||
- | * Week 3: Feature Extraction in Machine Learning: PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: {{: | + | * Week 3: {{: |
- | * Week 4-5: Discriminative Models: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing | + | * Week 4-5: {{: |
- | * Week 6: Artificial Neural Networks and Deep Learning: | + | * Week 6: {{: |
- | * Week 7: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models | + | * Week 7: {{: |
- | * Week 8: Generative Models and Parameter | + | * Week 8: {{: |
- | * Week 9: Graphical models: Bayesian Networks vs Markov random fields; | + | * Week 9: {{: |
lecture_notes.1569254785.txt.gz · Last modified: 2019/09/23 16:06 by hj