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Added new section - Artificial Intelligence
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Prakhar Srivastav 2016-03-11 12:09:43 -05:00
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@ -16,6 +16,7 @@ Table of Contents
- [Introduction to CS](#introduction-to-cs)
- [Machine Learning](#machine-learning)
- [Security](#security)
- [Artificial Intelligence](#artificial-intelligence)
- [Misc](#misc)
### Legend
@ -541,11 +542,6 @@ Courses
- [Lectures](https://work.caltech.edu/lectures.html)
- [Homework](https://work.caltech.edu/homeworks.html)
- [Textbook](https://work.caltech.edu/textbook.html)
- [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)
@ -588,11 +584,6 @@ Topics covered include probability theory and Bayesian inference; univariate dis
- [Readings](http://llcao.net/cu-deeplearning15/reading.html)
- [Assignments](http://llcao.net/cu-deeplearning15/programming_problem.html)
- [Lecture Notes](http://llcao.net/cu-deeplearning15/index.html)
- [6.868J](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/index.htm) **The Society of Mind** *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 an introduction, by Prof. [Marvin Minsky](http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0), to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.
- [Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/video-lectures/)
- [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/assignments/)
- [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/readings/)
-------
@ -651,7 +642,28 @@ and anti-analysis techniques.
- [Readings](https://www.eecs.umich.edu/courses/eecs588/readings.html)
-------
### Artificial Intelligence
- [6.868J](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/index.htm) **The Society of Mind** *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 an introduction, by Prof. [Marvin Minsky](http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0), to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.
- [Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/video-lectures/)
- [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/assignments/)
- [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/readings/)
- [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 4700](http://www.cs.cornell.edu/courses/CS4700/2014fa/) **Foundations of Artificial Intelligence** *Cornell 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" />
- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- [Assignments](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
- [Lectures](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
- [CS 6700](http://www.cs.cornell.edu/courses/CS6700/2013sp/) **Advanced Artificial Intelligence** *Cornell University* <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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
- [Syllabus](http://www.cs.cornell.edu/courses/CS6700/2013sp/lectures/CS6700-Overview_v2.pptx)
- [Lectures](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
- [Readings](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
-------
### Misc
- [AM 207](http://am207.org/) **Monte Carlo Methods and Stochastic Optimization** *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/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" />
- This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. All of this in Python!
@ -717,10 +729,6 @@ and anti-analysis techniques.
- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- [Assignments](http://www.cs.cornell.edu/courses/CS4670/2015sp/projects/projects.html)
- [Lectures](http://www.cs.cornell.edu/courses/CS4670/2015sp/lectures/lectures.html)
- [CS 4700](http://www.cs.cornell.edu/courses/CS4700/2014fa/) **Foundations of Artificial Intelligence** *Cornell 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" />
- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- [Assignments](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
- [Lectures](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
- [CS 4786](http://www.cs.cornell.edu/courses/CS4786/2015sp/index.htm) **Machine Learning for Data Science** *Cornell 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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
- Dimensionality reduction, such as principal component analysis (PCA) and the singular value decomposition (SVD), canonical correlation analysis (CCA), independent component analysis (ICA), compressed sensing, random projection, the information bottleneck. (We expect to cover some, but probably not all, of these topics).
@ -784,11 +792,6 @@ and anti-analysis techniques.
- [Syllabus](https://canvas.instructure.com/courses/904706/assignments/syllabus)
- [Lectures](https://canvas.instructure.com/courses/904706)
- [Assignments](https://canvas.instructure.com/courses/904706/assignments)
- [CS 6700](http://www.cs.cornell.edu/courses/CS6700/2013sp/) **Advanced Artificial Intelligence** *Cornell University* <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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
- [Syllabus](http://www.cs.cornell.edu/courses/CS6700/2013sp/lectures/CS6700-Overview_v2.pptx)
- [Lectures](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
- [Readings](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
- [CS 6840](http://www.cs.cornell.edu/courses/CS6840/2014sp/) **Algorithmic Game Theory** *Cornell 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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- Algorithmic Game Theory combines algorithmic thinking with game-theoretic, or, more generally, economic concepts. The course will study a range of topics at this interface
- [Syllabus](http://www.cs.cornell.edu/courses/CS6840/2014sp/)