Tidy up spaces
This commit is contained in:
Peter Thaleikis
parent
cb59cbd0dd
commit
7040cf7fca
100
README.md
100
README.md
|
|
@ -37,7 +37,7 @@ Courses
|
|||
- [CS 61C](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/) **Great Ideas in Computer Architecture (Machine Structures)** *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/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" />
|
||||
- The subjects covered in this course include: C and assembly language programming, translation of high-level programs into machine language, computer organization, caches, performance measurement, parallelism, CPU design, warehouse-scale computing, and related topics.
|
||||
- [Lecture Videos](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_?sort=titleSorter)
|
||||
- [Lecture Notes](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/#Calendar)
|
||||
- [Lecture Notes](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/#Calendar)
|
||||
- [Resources](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/#Resources)
|
||||
- [Old Exams](https://hkn.eecs.berkeley.edu/exams/course/CS/61C)
|
||||
- [CS 107](https://courseware.stanford.edu/pg/courses/lectures/371747) **Computer Organization & Systems** *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" />
|
||||
|
|
@ -55,13 +55,13 @@ Courses
|
|||
- [Labs-Assignments](http://computationstructures.org/exercises/cmos/lab.html)
|
||||
- [CS 162](http://cs162.eecs.berkeley.edu/) **Operating Systems and Systems Programming** *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/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" />
|
||||
- The purpose of this course is to teach the design of operating systems and operating systems concepts that appear in other advanced systems. Topics we will cover include concepts of operating systems, systems programming, networked and distributed systems, and storage systems, including multiple-program systems (processes, interprocess communication, and synchronization), memory allocation (segmentation, paging), resource allocation and scheduling, file systems, basic networking (sockets, layering, APIs, reliability), transactions, security, and privacy.
|
||||
- Operating Systems course by the Chair of EECS, UC Berkeley [David Culler](http://www.cs.berkeley.edu/~culler/)
|
||||
- [Lecture Videos](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iBDyz-ba4yDskqMDY6A1w_c) Spring 2015 lectures
|
||||
- [Lecture Notes](https://inst.eecs.berkeley.edu/~cs162/sp15/) Spring 2015 lectures
|
||||
- Operating Systems course by the Chair of EECS, UC Berkeley [David Culler](http://www.cs.berkeley.edu/~culler/)
|
||||
- [Lecture Videos](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iBDyz-ba4yDskqMDY6A1w_c) Spring 2015 lectures
|
||||
- [Lecture Notes](https://inst.eecs.berkeley.edu/~cs162/sp15/) Spring 2015 lectures
|
||||
- [CS 168](https://inst.eecs.berkeley.edu/~cs168/fa14/) **Introduction to the Internet: Architecture and Protocols** *UC Berkeley* <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 is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
|
||||
- [Lecture Notes & Assignments](https://inst.eecs.berkeley.edu/~cs168/fa14/class.html)
|
||||
- [Discussion Notes](https://inst.eecs.berkeley.edu/~cs168/fa14/)
|
||||
- This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
|
||||
- [Lecture Notes & Assignments](https://inst.eecs.berkeley.edu/~cs168/fa14/class.html)
|
||||
- [Discussion Notes](https://inst.eecs.berkeley.edu/~cs168/fa14/)
|
||||
- [CS 179](http://courses.cms.caltech.edu/cs179/) **GPU Programming** *Caltech* <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 cover programming techniques for the GPU. The course will introduce NVIDIA's parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
|
||||
- [Assignments](http://courses.cms.caltech.edu/cs179/)
|
||||
|
|
@ -111,10 +111,10 @@ Courses
|
|||
- [Lecture Notes](http://comp.uark.edu/~wingning/csce3613/CourseNote3613.html)
|
||||
- [Readings](http://comp.uark.edu/~wingning/csce3613/Link3613.html)
|
||||
- [CSCI-UA.0202: Operating Systems (Undergrad)](http://www.cs.nyu.edu/~mwalfish/classes/15sp/index.html) **Operating Systems** *NYU* <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" />
|
||||
- NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process scheduling, file system, ect. It also includes some recommended reading materials. What's more, there are a series of hands-on lab materials, helping you easily understand OS.
|
||||
- [Assignments](http://www.cs.nyu.edu/~mwalfish/classes/15sp/labs.html)
|
||||
- [Lectures](http://www.cs.nyu.edu/~mwalfish/classes/15sp/syllabus.html)
|
||||
- [Old Exams](http://www.cs.nyu.edu/~mwalfish/classes/15sp/exams.html)
|
||||
- NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process scheduling, file system, ect. It also includes some recommended reading materials. What's more, there are a series of hands-on lab materials, helping you easily understand OS.
|
||||
- [Assignments](http://www.cs.nyu.edu/~mwalfish/classes/15sp/labs.html)
|
||||
- [Lectures](http://www.cs.nyu.edu/~mwalfish/classes/15sp/syllabus.html)
|
||||
- [Old Exams](http://www.cs.nyu.edu/~mwalfish/classes/15sp/exams.html)
|
||||
- [CSCI 360](http://compsci.hunter.cuny.edu/~sweiss/course_materials/csci360/csci360_f14.php) **Computer Architecture 3** *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" />
|
||||
- A course that covers cache design, buses, memory hierarchies, processor-peripheral interfaces, and multiprocessors, including GPUs.
|
||||
- [CSCI 493.66](http://compsci.hunter.cuny.edu/~sweiss/course_materials/csci493.66/csci493.66_spr12.php) **UNIX System Programming (formerly UNIX Tools)** *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" />
|
||||
|
|
@ -171,8 +171,8 @@ Courses
|
|||
- [Lecture Notes](https://www.cs.cmu.edu/~410/lecture.html)
|
||||
- [Assignments](https://www.cs.cmu.edu/~410/p1/proj1.html)
|
||||
- [15-418](http://15418.courses.cs.cmu.edu/spring2015/) **Parallel Computer Architecture and Programming** *Carnegie-Mellon 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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design.
|
||||
- [Assignments](http://15418.courses.cs.cmu.edu/spring2015/exercises)
|
||||
- The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design.
|
||||
- [Assignments](http://15418.courses.cs.cmu.edu/spring2015/exercises)
|
||||
- [Lecture Notes](http://15418.courses.cs.cmu.edu/spring2015/reading)
|
||||
- [Lecture Videos](https://scs.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=%22a5862643-2416-49ef-b46b-13465d1b6df0%22)
|
||||
- [Readings](http://15418.courses.cs.cmu.edu/spring2015/reading)
|
||||
|
|
@ -627,7 +627,7 @@ Courses
|
|||
- [Labs and Assignments](http://cs109.github.io/2014/pages/homework.html)
|
||||
- [2014 Lectures](http://cs109.github.io/2014/)
|
||||
- [2013 Lectures](http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml) *(slightly better)*
|
||||
- [CS 156](https://work.caltech.edu/telecourse.html) **Learning from Data** *Caltech* <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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- [CS 156](https://work.caltech.edu/telecourse.html) **Learning from Data** *Caltech* <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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
|
||||
- [Lectures](https://work.caltech.edu/lectures.html)
|
||||
- [Homework](https://work.caltech.edu/homeworks.html)
|
||||
|
|
@ -650,9 +650,9 @@ Courses
|
|||
- [Lecture Videos](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)
|
||||
- [Github Page](http://cs231n.github.io/)
|
||||
- [CS 287](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/) **Advanced Robotics** *UC Berkeley* <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" />
|
||||
- The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
|
||||
- [Lectures Notes](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#syllabus)
|
||||
- [Assignments](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#assignments)
|
||||
- The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
|
||||
- [Lectures Notes](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#syllabus)
|
||||
- [Assignments](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#assignments)
|
||||
- [CS 395T](http://www.nr.com/CS395T/) **Statistical and Discrete Methods for Scientific Computing** *University of Texas* <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" />
|
||||
- Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++.
|
||||
Topics covered include probability theory and Bayesian inference; univariate distributions; Central Limit Theorem; generation of random deviates; tail (p-value) tests; multiple hypothesis correction; empirical distributions; model fitting; error estimation; contingency tables; multivariate normal distributions; phylogenetic clustering; Gaussian mixture models; EM methods; maximum likelihood estimation; Markov Chain Monte Carlo; principal component analysis; dynamic programming; hidden Markov models; performance measures for classifiers; support vector machines; Wiener filtering; wavelets; multidimensional interpolation; information theory.
|
||||
|
|
@ -703,9 +703,9 @@ Topics covered include probability theory and Bayesian inference; univariate dis
|
|||
- [Lecture Videos](http://www.ischool.berkeley.edu/newsandevents/audiovideo/webcast/21963)
|
||||
- [Previous Years coursepage](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)
|
||||
- [Machine Learning: 2014-2015](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) *University of Oxford* <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" />
|
||||
- The course focusses on neural networks and uses the [Torch](https://github.com/torch/torch7/wiki/Cheatsheet) deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, back-propagation, convolutional neural networks, max-margin learning, siamese networks, recurrent neural networks, LSTMs, hand-writing with recurrent neural networks, variational autoencoders and image generation and reinforcement learning
|
||||
- [Lectures and Assignments](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
|
||||
- [Source code](https://github.com/oxford-cs-ml-2015/)
|
||||
- The course focusses on neural networks and uses the [Torch](https://github.com/torch/torch7/wiki/Cheatsheet) deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, back-propagation, convolutional neural networks, max-margin learning, siamese networks, recurrent neural networks, LSTMs, hand-writing with recurrent neural networks, variational autoencoders and image generation and reinforcement learning
|
||||
- [Lectures and Assignments](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
|
||||
- [Source code](https://github.com/oxford-cs-ml-2015/)
|
||||
- [StatLearning](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) **Intro to Statistical Learning** *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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
|
||||
- This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.
|
||||
- The lectures cover all the material in [An Introduction to Statistical Learning, with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) which is a more approachable version of the [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) (or ESL) book.
|
||||
|
|
@ -715,11 +715,11 @@ Topics covered include probability theory and Bayesian inference; univariate dis
|
|||
- [Lectures](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)
|
||||
- [Project Ideas and Datasets](http://www.cs.cmu.edu/~tom/10701_sp11/proj.shtml)
|
||||
- [10-708](http://www.cs.cmu.edu/~epxing/Class/10708-14/index.html) **Probabilistic Graphical Models** *Carnegie Mellon 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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides a unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
|
||||
- [Lecture Videos](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- [Assignments](http://www.cs.cmu.edu/~epxing/Class/10708-14/homework.html)
|
||||
- [Lecture notes](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- [Readings](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides a unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
|
||||
- [Lecture Videos](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- [Assignments](http://www.cs.cmu.edu/~epxing/Class/10708-14/homework.html)
|
||||
- [Lecture notes](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- [Readings](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
|
||||
- [11-785](http://deeplearning.cs.cmu.edu/) **Deep Learning** *Carnegie Mellon 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/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. *Lectures* and *Reading Notes* are available on the page.
|
||||
- [CS246](http://web.stanford.edu/class/cs246/) **Mining Massive Data Sets** *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/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" />
|
||||
|
|
@ -800,11 +800,11 @@ and anti-analysis techniques.
|
|||
-------
|
||||
### Artificial Intelligence
|
||||
|
||||
- [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 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/)
|
||||
|
|
@ -815,10 +815,10 @@ and anti-analysis techniques.
|
|||
- [Lectures](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [Readings](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [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/)
|
||||
- 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/)
|
||||
|
||||
-------
|
||||
### Computer Graphics
|
||||
|
|
@ -831,9 +831,9 @@ and anti-analysis techniques.
|
|||
- [Lectures](https://alliance.seas.upenn.edu/~cis581/wiki/index.php?title=Schedule)
|
||||
- [Assignments](https://alliance.seas.upenn.edu/~cis581/wiki/index.php?title=Projects)
|
||||
- [CMU 462](http://15462.courses.cs.cmu.edu/fall2015) **Computer Graphics** *Carnegie Mellon 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/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 provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis.
|
||||
- [Lectures and Readings](http://15462.courses.cs.cmu.edu/fall2015/reading)
|
||||
- [Assignments and Quizes](http://15462.courses.cs.cmu.edu/fall2015/exercises)
|
||||
- This course provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis.
|
||||
- [Lectures and Readings](http://15462.courses.cs.cmu.edu/fall2015/reading)
|
||||
- [Assignments and Quizes](http://15462.courses.cs.cmu.edu/fall2015/exercises)
|
||||
- [CS 378](https://github.com/ut-cs378-vision-2014fall/course-info) **3D Reconstruction with Computer Vision** *UTexas* <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" />
|
||||
- In this lab-based class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
|
||||
- [Lectures](https://github.com/ut-cs378-vision-2014fall/course-info/tree/master/meeting-notes)
|
||||
|
|
@ -862,10 +862,10 @@ and anti-analysis techniques.
|
|||
-------
|
||||
### Misc
|
||||
- [AM 207](http://am207.github.io/2016/index.html) **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!
|
||||
- [Lecture Videos](http://cm.dce.harvard.edu/2015/02/24104/publicationListing.shtml)
|
||||
- [Assignments](http://am207.github.io/2016/homework.html)
|
||||
- [Lecture Notes](http://am207.github.io/2016/lectures.html)
|
||||
- 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!
|
||||
- [Lecture Videos](http://cm.dce.harvard.edu/2015/02/24104/publicationListing.shtml)
|
||||
- [Assignments](http://am207.github.io/2016/homework.html)
|
||||
- [Lecture Notes](http://am207.github.io/2016/lectures.html)
|
||||
- [CS 75](http://ocw.tufts.edu/Course/75) **Introduction to Game Development** *Tufts 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" />
|
||||
- The course taught by [Ming Y. Chow](http://mchow01.github.io) teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game physics, sprites, animation, game development methodology, sound, testing, MMORPGs and online games, and addressing mobile development in Android, HTML5, and iOS. Most to all of the development is focused on PyGame for learning principles
|
||||
- [Text Lectures](http://ocw.tufts.edu/Course/75/Learningunits)
|
||||
|
|
@ -882,8 +882,8 @@ and anti-analysis techniques.
|
|||
- [CS 108](http://web.stanford.edu/class/archive/cs/cs108/cs108.1092/) **Object Oriented System Design** *Stanford* <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" />
|
||||
- Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams.
|
||||
- [CS 168](https://inst.eecs.berkeley.edu/~cs168/fa15/) **Computer Networks** *UC Berkeley*<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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python).
|
||||
- You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.
|
||||
- This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python).
|
||||
- You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.
|
||||
- [CS 193a](http://web.stanford.edu/class/cs193a/lectures.shtml) **Android App Development, Spring 2016** *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" />
|
||||
- Course Description: This course provides an introduction to developing applications for the Android mobile platform.
|
||||
- Prerequisite: CS 106B or equivalent. Java experience highly recommended. OOP highly recommmended.
|
||||
|
|
@ -891,11 +891,11 @@ and anti-analysis techniques.
|
|||
- Videos: Videos list can be found [here](http://web.stanford.edu/class/cs193a/videos.shtml)
|
||||
- Other materials: Some codes, handsout, homework ..... and lecture notes are not downloadable on the site due to login requirement. Please head to my Github repo [here](https://github.com/VoLuong/Materials-CS193A-Android-App-Development-Standford) to download them.
|
||||
- [CS 193p](https://itunes.apple.com/us/course/developing-ios-7-apps-for/id733644550) **Developing Applications for iOS** *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" />
|
||||
- Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
|
||||
- Prerequisites: C language and object-oriented programming experience
|
||||
- Recommended: [Programming Abstractions](https://itunes.apple.com/us/course/programming-abstractions/id495054099)
|
||||
- [Updated courses for iOS8 - Swift](https://itunes.apple.com/us/course/developing-ios-8-apps-swift/id961180099)
|
||||
- [Updated courses for iOS9 - Swift](https://itunes.apple.com/us/course/developing-ios-9-apps-swift/id1104579961)
|
||||
- Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
|
||||
- Prerequisites: C language and object-oriented programming experience
|
||||
- Recommended: [Programming Abstractions](https://itunes.apple.com/us/course/programming-abstractions/id495054099)
|
||||
- [Updated courses for iOS8 - Swift](https://itunes.apple.com/us/course/developing-ios-8-apps-swift/id961180099)
|
||||
- [Updated courses for iOS9 - Swift](https://itunes.apple.com/us/course/developing-ios-9-apps-swift/id1104579961)
|
||||
- [CS 223A](https://see.stanford.edu/Course/CS223A) **Introduction to Robotics** *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" />
|
||||
- The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
|
||||
- [CS 262a](http://www.cs.berkeley.edu/~brewer/cs262/) **Advanced Topics in Computer Systems** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
|
|
@ -1011,8 +1011,8 @@ and anti-analysis techniques.
|
|||
- This is an introductory course on Networking for graduate students. It follows a top-down approach to teaching Computer Networks, so it starts with the Application layer which most of the students are familiar with and as the course unravels we learn more about transport, network and link layers of the protocol stack.
|
||||
- As far as prerequisites are concerned - basic computer, programming and probability theory background is required.
|
||||
- The course site contains links to the lecture videos, reading material and assignments.
|
||||
|
||||
|
||||
|
||||
|
||||
-----
|
||||
### Statistics
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue