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Added Harvard CS 224 and CS229r, Stanford 261. (#208)

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* Added Harvard CS 224 and CS229r, Stanford 261.

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CS 224: Advanced Algorithms(Harvard University)
CS 229r: Algorithms for Big Data (Harvard University)
CS 261 A second course in Algorithms - Stanford University

Updated
6.854 Advanced Algorithms MIT video lectures

* Removed extra period character

* Added 16s-4102 Algorithms (University of Virginia)
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Yogesh Paul 2016-09-29 23:49:18 +05:30 committed by Prakhar Srivastav
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@ -321,9 +321,18 @@ Courses
*CUNY Hunter College*
- It is currently an intensive introduction to program development and problem solving. Its emphasis is on the process of designing, implementing, and evaluating small-scale programs. It is not supposed to be a C++ programming course, although much of the course is spent on the details of C++. C++ is an extremely large and complex programming language with many features that interact in unexpected ways. One does not need to know even half of the language to use it well.
- [Lectures and Assignments](http://compsci.hunter.cuny.edu/~sweiss/course_materials/csci135/csci135_36_fall12.php)
- [16s-4102](http://www.cs.virginia.edu/~shelat/16s-4102/) **Algorithms** *University of Virginia* <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" />
- [Lecture Videos & Homeworks](http://www.cs.virginia.edu/~shelat/16s-4102/) ([Youtube](https://www.youtube.com/channel/UCxXYk53cSZof2bR_Ax0uJYQ/videos))
- [CS 224](http://people.seas.harvard.edu/~minilek/cs224/fall14/index.html) **Advanced Algorithms** *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" />
- CS 224 is an advanced course in algorithm design, and topics we will cover include the word RAM model, data structures, amortization, online algorithms, linear programming, semidefinite programming, approximation algorithms, hashing, randomized algorithms, fast exponential time algorithms, graph algorithms, and computational geometry.
- [Lecture Videos](http://people.seas.harvard.edu/~minilek/cs224/fall14/lec.html) ([Youtube](https://www.youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf))
- [Assignments](http://people.seas.harvard.edu/~minilek/cs224/fall14/hmwk.html)
- [CSCI 235](http://compsci.hunter.cuny.edu/~sweiss/courses/csci235.php) **Software Design and Analysis II** *CUNY Hunter College* <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" />
- Introduces algorithms for a few common problems such as sorting. Practically speaking, it furthers the students' programming skills with topics such as recursion, pointers, and exception handling, and provides a chance to improve software engineering skills and to give the students practical experience for more productive programming.
- [Lectures and Assignments](http://compsci.hunter.cuny.edu/~sweiss/course_materials/csci235/csci235_f14.php)
- [CS 261](http://theory.stanford.edu/~tim/w16/w16.html) **A Second Course in Algorithms** *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" />
- Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms.
- [Lecture Notes, Videos & Assignments](http://theory.stanford.edu/~tim/w16/w16.html) ([Youtube](https://www.youtube.com/playlist?list=PLEGCF-WLh2RJh2yDxlJJjnKswWdoO8gAc))
- [CSCI 335](http://compsci.hunter.cuny.edu/~sweiss/courses/csci335.php) **Software Design and Analysis III** <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" />
*CUNY Hunter College*
- This includes the introduction of hashes, heaps, various forms of trees, and graphs. It also revisits recursion and the sorting problem from a higher perspective than was presented in the prequels. On top of this, it is intended to introduce methods of algorithmic analysis.
@ -376,6 +385,7 @@ Courses
- [Old Exams](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/exams/)
- [6.854J/18.415J](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2005/index.htm) **Advanced Algorithms** *MIT* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /><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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures. The need for efficient algorithms arises in nearly every area of computer science. But the type of problem to be solved, the notion of what algorithms are "efficient,'' and even the model of computation can vary widely from area to area. In this second class in algorithms, we will survey many of the techniques that apply broadly in the design of efficient algorithms, and study their application in a wide range of application domains and computational models. The goal is for the class to be broad rather than deep. Our plan is to touch upon the following areas. This is a tentative list of topics that might be covered in the class; we will select material adaptively based on the background, interests, and rate of progress of the students.
- [Lecture Videos - Spring 2016](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c)
- [Lecture Notes](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2005/lecture-notes/)
- [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2005/assignments/)
- [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2005/readings/)
@ -599,6 +609,10 @@ 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 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))
- [Assignments](http://people.seas.harvard.edu/~minilek/cs229r/fall15/hmwk.html)
- [CS 231n](http://cs231n.stanford.edu/) **Convolutional Neural Networks for Visual Recognition** *Stanford University* <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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
- Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
- [Lecture Notes](http://cs231n.stanford.edu/syllabus.html)