lecture_notes
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lecture_notes [2017/12/23 01:10] – hj | lecture_notes [2018/03/13 17:31] – hj | ||
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- | * Weeks 1-2: A. Machine Learning (basic concepts); B. Math foundation | + | * 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: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models | + | * Week 4: {{: |
- | * Week 5-6: Discriminative Models | + | * Week 5-6: {{: |
- | * Week 7: Artificial Neural Networks and Deep Learning: | + | * Week 7: {{: |
- | * Week 8: Generative Models and Parameter Estimation: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, | + | * Week 8: {{: |
- | * Week 9: Graphical models: Bayesian Networks vs Markov random fields; | + | * Week 9: {{: |
lecture_notes.txt · Last modified: 2019/08/22 21:23 by hj