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@ -494,6 +494,11 @@ Courses
- [Slides](http://cs109.github.io/2014/pages/schedule.html)
- [Labs and Assignments](http://cs109.github.io/2014/pages/homework.html)
- [2013 Lectures](http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml) *(slightly better)*
- [CS 188](http://ai.berkeley.edu/home.html) **Introduction to Artificial Intelligence** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20 " height="20" alt="Lecture Notes" title="Lecture Notes" />
- This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
- [Lectures](http://ai.berkeley.edu/lecture_videos.html)
- [Projects](http://ai.berkeley.edu/project_overview.html)
- [Exams](http://ai.berkeley.edu/exams.html)
- [CS 224d](http://cs224d.stanford.edu/) **Deep Learning for Natural Language Processing** *Stanford University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
- Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
- [Syllabus](http://cs224d.stanford.edu/syllabus.html)