Processing of Language and Image. We Will Discuss Nlp for the Web Where These Problems Are Prominent. Particularly, We Will Discuss and Compare Methods Based on Bayesian Inference (E.G. Topic Models), Convex Linear Structured Prediction (E.G. Conditional Random Fields (Crfs)) and Deep Neural Networks (Dnns) for Multi Task and Problems at the Main Focus of the Resarch Community with a Particular Focus on Language Processing in the Web, Where Language Is Processed Jointly with Other Modalities Such as Author Properties, Imgaes and Feedback from Other Users. a Particular Ateention Will Be and Advanced Approximation Methods in Graphical Models. Given to Covering Advanced Computational Methods in Deep in the End of the Course:
1. the Student Will Be Familiar with Advanced Topics in Natural Language Processing, with a Focus on Multilingual and Cross-Lingual Processing on the Web.
2. the Student Will Be Familiar with Advanced Papers in the Field.
3. the Student Will Implement a Project Based on the Course Material, Using Real World Data.