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lecture_notes [2018/03/06 17:41] hjlecture_notes [2018/03/13 17:31] hj
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   * Week 8: {{:ml7-generative.pdf|Generative Models and Parameter Estimation}}: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, Markov chains, GMM.    * Week 8: {{:ml7-generative.pdf|Generative Models and Parameter Estimation}}: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, Markov chains, GMM. 
  
-  * Week 9: Graphical models: Bayesian Networks vs Markov random fields;  Conditional independence; Inference in graphical models; belief propagation; variational inference+  * Week 9: {{:ml8-graphicalmodel.pdf|Graphical models}}: Bayesian Networks vs Markov random fields;  Conditional independence; Inference in graphical models; belief propagation; variational inference
lecture_notes.txt · Last modified: 2019/08/22 21:23 by hj