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--- lecture_notes [2018/02/27 19:48] – hj
+++ lecture_notes [2018/03/13 17:31] – hj
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   * Week 7: {{: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 (Extra reading assignment: [[http://sebastianruder.com/optimizing-gradient-descent/|other gradient descent algorithms for deep learning]]).
-  * Week 8: 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