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@ -637,6 +637,10 @@ Courses
- 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)
- [Lectures and Assignments](http://cs224d.stanford.edu/syllabus.html)
- [CS 224w](http://web.stanford.edu/class/cs224w/) **Machine Learning with Graphs** *Stanford University*
- Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. **Topics include**: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
- [Lectures videos](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)
- [Lecture notes, assignments, ...](http://web.stanford.edu/class/cs224w/)
- [CS 229r](http://people.seas.harvard.edu/~minilek/cs229r/fall15/index.html) **Algorithms for Big Data** *Harvard 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" />
- Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.
- [Lectures](http://people.seas.harvard.edu/~minilek/cs229r/fall15/lec.html) ([Youtube](https://www.youtube.com/playlist?list=PL2SOU6wwxB0v1kQTpqpuu5kEJo2i-iUyf))