lecture_notes
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lecture_notes [2018/01/09 19:42] – hj | lecture_notes [2019/08/22 21:23] (current) – hj | ||
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- | * Weeks 1-2: A. {{: | + | * Weeks 1-2: A. Machine Learning (basic concepts); B. Math foundation: probabilities and statistics, Bayes theorem, |
- | * Week 3: Feature Extraction in Machine Learning: PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: | + | * Week 3: Feature Extraction in Machine Learning PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: a tutorial paper on PCA, a paper on ML). |
* Week 4: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models | * Week 4: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models | ||
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* Week 5-6: Discriminative Models: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing | * Week 5-6: Discriminative Models: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing | ||
- | * Week 7: Artificial Neural Networks and Deep Learning: | + | * Week 7: Artificial Neural Networks and Deep Learning: |
* Week 8: Generative Models and Parameter Estimation: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, | * Week 8: Generative Models and Parameter Estimation: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, | ||
* Week 9: Graphical models: Bayesian Networks vs Markov random fields; | * Week 9: Graphical models: Bayesian Networks vs Markov random fields; |
lecture_notes.1515526952.txt.gz · Last modified: 2018/01/09 19:42 by hj