lecture_notes
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+ | * Weeks 1-2: A. {{: | ||
- | * Weeks 1-2: A. {{:ml1-intro.pdf|Machine Learning | + | * Week 3: {{:ml3-feature.pdf|Feature Extraction in Machine Learning}}: |
- | * Week 3: Feature Extraction in Machine Learning: PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: {{:tutorial-pca.pdf|a tutorial paper on PCA}}, {{:ml-blackart.pdf|a paper on ML}}). | + | * Week 4-5: {{:ml5-discriminative.pdf|Discriminative Models}}: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing |
- | * Week 4-5: Discriminative Models: Statistical | + | * Week 6: {{:ml6-ann.pdf|Artificial Neural Networks and Deep Learning}}: artificial neural networks (model structure and learning criteria); error back-propagation algorithm; fine-tuning tricks; Convolutional neural networks; Recurrent neural networks |
- | * Week 6: Artificial Neural Networks and Deep Learning: | + | * Week 7: {{:ml4-decision-rule.pdf|Bayesian Decision Rule}}: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models |
- | * Week 7: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models | + | * Week 8: {{:ml7-generative.pdf|Generative Models and Parameter Estimation}} : generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, |
- | * Week 8: Generative Models and Parameter Estimations | + | * Week 9: {{:ml8-graphicalmodel.pdf|Graphical models}}: Bayesian Networks vs Markov random fields; |
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- | * Week 9: Graphical models: Bayesian Networks vs Markov random fields; | + |
lecture_notes.1569254733.txt.gz · Last modified: 2019/09/23 16:05 by hj