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<title>iTunes-U and Coursera </title>
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<div class="menu-category">个人主页</div>
<div class="menu-item"><a href="index.html">首页</a></div>
<div class="menu-item"><a href="resume.html">简历</a></div>
<div class="menu-item"><a href="document.html">文档</a></div>
<div class="menu-category">投机交易</div>
<div class="menu-item"><a href="itunes-u.html">课程</a></div>
<div class="menu-item"><a href="logs.html">日志</a></div>
<div class="menu-item"><a href="system.html">系统</a></div>
<div class="menu-item"><a href="download.html">下载</a></div>
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<h1>iTunes-U and Coursera </h1>
</div>
<h2>Finance </h2>
<ul>
<li><p><a href="http://cgt.columbia.edu/videos/">Columbia The Committee on Global Thought</a> <tt> Done </tt> <br /> <a href="http://cgt.columbia.edu/videos/a_new_disorder/">F inancial Design in the Aftermath</a> | <a href="http://cgt.columbia.edu/videos/myths_markets_and_main_street/">Economic Myths, Markets, and Main Street</a> | <a href="http://cgt.columbia.edu/videos/long-term_investing_an_optimal_strategy_in_short-term_oriented_markets26/">Theoretical Analysis of Value and Momentum Strategies</a> </p>
</li>
<li><p><a href="https://class.coursera.org/fe-001/class/index">MOOC Columbia</a> Financial Engineering and Risk Management , Martin Haugh, Garud Iyengar</p>
</li>
<li><p><a href="http://ocw.mit.edu/courses/sloan-school-of-management/15-401-finance-theory-i-fall-2008/index.htm">MIT 15.401</a> Finance Theory I <a href="https://itunes.apple.com/us/itunes-u/financial-theory-video/id428500350">iTunes</a> | <a href="http://ocw.mit.edu/courses/sloan-school-of-management/15-401-finance-theory-i-fall-2008/video-lectures-and-slides/">MITOC Video</a> <tt>Local Lecture</tt> <br /> </p>
</li>
<li><p><a href="http://oyc.yale.edu/economics/econ-251">Yale ECON251</a> Financial Theory, John Geanakoplos, <a href="http://oyc.yale.edu/economics/econ-251#sessions">Fall 2009 Video</a> | <a href="https://itunes.apple.com/us/itunes-u/financial-theory-video/id428500350">iTunes</a> <br /></p>
</li>
<li><p><a href="http://oyc.yale.edu/economics/econ-252-08">Yale ECON252</a> Financial Markets Robert Shiller.<a href="http://oyc.yale.edu/economics/econ-252-08#sessions">Spring 2011</a> <tt> Introduction Broad Topics | Lack in Depths | Done </tt></p>
</li>
<li><p><a href="https://class.coursera.org/compfinance-003/class/index">MOOC W</a> Introduction to Computational Finance and Financial Econometrics Eric Zivot</p>
</li>
</ul>
<h2>Algorithms/Optimization </h2>
<ul>
<li><p><a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00-introduction-to-computer-science-and-programming-fall-2008/video-lectures/">MIT 6.00</a> Introduction to Computer Science and Programming Fall 2008 <a href="https://itunes.apple.com/cn/itunes-u/introduction-to-computer-science/id341597455">iTunes <tt>Local Lecture</tt></a> <br />
<a href="https://developers.google.com/edu/python/introduction">Google Python Training</a> <a href="http://docs.python.org/2.7/tutorial/">Python Docs</a> Good Course For Programming Overall. </p>
</li>
<li><p><a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/">MIT 6.006</a> Introduction to Algorithms, Erik Demaine and Srinivas Devadas, Fall 2011 iTunes </p>
</li>
<li><p><a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/video-lectures/">MIT 6.042J</a> Mathematics for Computer Science, Tom Leighton <a href="https://itunes.apple.com/us/itunes-u/mathematics-for-computer-science/id503873536">iTunes</a> <tt>Local Lecture</tt> <br />
Number Theory is Boring| Proof, Induction is OK| Sum are well explained | Graph and Probability Fantastic | <tt>Tom is a very good teacher. </tt></p>
</li>
<li><p><a href="https://class.coursera.org/scicomp-001/lecture/index">MOOC W</a> High Performance Scientific Computing | Linux, Python, Amazone EC2, <a href="https://bitbucket.org/rjleveque/uwhpsc.git">GitHub</a> Skipped Fotran90 MPI, <tt>Local EC2 Video | Done</tt> </p>
</li>
<li><p>MOOC Stanford Algorithms: Design and Analysis, <a href="https://www.coursera.org/course/algo">Part 1</a> <a href="https://www.coursera.org/course/algo2">Part 2</a></p>
</li>
<li><p>Linear Dynamical Systems <a href="http://stanford.edu/~boyd/">Stephen Boyd</a> </p>
</li>
<li><p>Stanford EE364A Convex Optimization Stephen Boyd </p>
</li>
<li><p>Stanford EE364B Convex Optimization Stephen Boyd </p>
</li>
<li><p><a href="http://persson.berkeley.edu/128A/">Math128A</a> Numerical Analysis, Fall 2012 <a href="https://itunes.apple.com/us/itunes-u/mathematics-128a-001-fall/id556236572">itunes-U</a> <tt> Local Lecture </tt> </p>
</li>
<li><p><a href="https://www.coursera.org/course/optimization">MOOC Melbourne</a> Discrete Optimization Pascal Van Hentenryck</p>
</li>
</ul>
<h2>Statistics/Data Science </h2>
<ul>
<li><p><a href="http://datascienc.es/spring-2011-course/">CS194-16</a> Introduction to Data Science, Sprint 2011, <tt>Tons of resource</tt></p>
</li>
<li><p><a href="https://class.coursera.org/datasci-001/class/index">MOOC W</a> Introduction to Data Science Bill Howe <a href="https://github.com/uwescience/datasci_course_materials.git">GitHub</a> </p>
</li>
<li><p><a href="http://had.co.nz/stat645/">STAT645</a> Data Visualisation Rice University, Spring 2011, <tt>Lecture Only | Local Lecture </tt> </p>
</li>
<li><p><a href="https://class.coursera.org/introstats-001/class/index">MOOC Toronto</a> Statistics: Making Sense of Data <tt> Local R video tutrial </tt> | Stats Testing Part is Intresting| <tt>Done</tt> </p>
</li>
<li><p>Statistics 110: Probability Harvard <a href="https://itunes.apple.com/cn/course/statistics-110-probability/id502492375">itunes-U</a> <tt> Old School Teachining, NO Lecture notes </tt> <br />
<a href="http://en.wikipedia.org/wiki/Birthday_problem">Birthday Problem</a> |Monty Hall |<a href="http://en.wikipedia.org/wiki/List_of_paradoxes">List_of_paradoxex</a> |<a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial_distribution</a> </p>
</li>
<li><p><a href="https://class.coursera.org/compmethods-003/class/index">MOOC W</a> Computational Methods for Data Analysis Nathan Kutz, Radar DSP Intresting Wavelet Concept Well Explained|<tt>Nathan is a very good teacher </tt> <br />
Sister Course <a href="https://class.coursera.org/scientificcomp-004/class/index">Scientific Computing</a> Mainly on Numerical Methods and Matlab <tt> Lecture Notes for Refence | Done</tt> </p>
</li>
<li><p><a href="https://www.coursera.org/course/compdata">MOOC Johns Hopkins</a> Computing for Data Analysis Roger Peng </p>
</li>
<li><p>MIT 6.041 Probabilistic Systems Analysis and Applied Probability </p>
</li>
</ul>
<h2>Machine Learning </h2>
<ul>
<li><p><a href="http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml">CM 10-701</a> Machine Learning Tom Mitchell, Spring 2011 <tt>Watch online, Video +Slide</tt></p>
</li>
<li><p><a href="http://ai.stanford.edu/~ang/courses.html">Stanford CS229</a> Machine Learning <a href="https://itunes.apple.com/us/itunes-u/machine-learning/id384233048">iTunes-U</a> <a href="https://class.coursera.org/ml-003/class">Coursera</a> </p>
</li>
<li><p><a href="http://inst.eecs.berkeley.edu/~cs188/fa11/lectures.html">CS188</a> Artificial Intelligence, Fall 2011 <tt>Lecture Local 2012 </tt> </p>
</li>
<li><p><a href="http://ocw.mit.edu/courses/sloan-school-of-management/15-062-data-mining-spring-2003/syllabus/">15.062</a> Data Mining, 2003, MIT <tt>Lecture Only</tt> </p>
</li>
<li><p><a href="https://class.coursera.org/pgm-003/class/index">MOOC MIT</a> Probabilistic Graphical Models, Daphne Koller </p>
</li>
<li><p><a href="https://class.coursera.org/nlangp-001/class/index">MOOC Columbia</a> Natural Language Processing Michael Collins</p>
</li>
<li><p><a href="https://class.coursera.org/neuralnets-2012-001/class/index">MOOC Toronto</a> Neural Networks for Machine Learning </p>
</li>
<li><p><a href="http://www.cc.gatech.edu/~agray/4245fall10/">GT 4245</a> Introduction to Data Mining and Analysis, Book <a href="http://www-stat.stanford.edu/~tibs/ElemStatLearn/">The Elements of Statistical Learning</a></p>
</li>
<li><p><a href="http://www.stanford.edu/class/cs246/index.html">Stanford CS246</a> Mining Massive Data Sets, Jure Leskovec </p>
</li>
</ul>
<h2>Electric Enginering ASIC </h2>
<ul>
<li><p><a href="https://class.coursera.org/vlsicad-001/class">VLSI CAD: Logic to Layout</a> Rob A. Rutenbar Illinois | <tt>Selected Lecture Only</tt>: ASIC Placement, ASIC Routing, Timing Analysis</p>
</li>
</ul>
<h2>Social Science </h2>
<ul>
<li><p><a href="https://class.coursera.org/modelthinking-004/class/index">MOOC Michigan</a> Model Thinking </p>
</li>
<li><p><a href="https://class.coursera.org/gametheory-2012-002/lecture/index">MOOC Stanford</a> Game Theory 7 Weeks, <tt>Local Video for Later </tt></p>
</li>
<li><p><a href="https://class.coursera.org/behavioralecon-001/lecture/index">MOOC Duke</a> A Beginner's Guide to Irrational Behavior Dan Ariely | <tt> Good lecture, Local Video for Later </tt> </p>
</li>
</ul>
<h2>Others </h2>
<ul>
<li><p>15.084J Nonlinear Programming <a href="http://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/lecture-notes/"><tt>Lecture Only</tt></a></p>
</li>
<li><p><a href="https://class.coursera.org/maththink-002/class/index">MIT: Introduction to Mathematical Thinking</a> Keith Devlin <tt> UnderGraduate and Dropout</tt> </p>
</li>
<li><p><a href="https://class.coursera.org/organalysis-2012-001/class/index">MIT: Organizational Analysis</a> Daniel McFarland <tt> Dropout </tt> </p>
</li>
<li><p>MIT 18.085/6 Mathematical Methods for Engineers I/II <a href="https://itunes.apple.com/cn/itunes-u/computational-science-engineering/id354869177">iTunes</a> <tt> Pure Math, Poor Quality Video, Dropout on Lecture1</tt> <a href="http://ocw.mit.edu/courses/mathematics/18-086-mathematical-methods-for-engineers-ii-spring-2006/index.htm">PART II</a> <a href="https://itunes.apple.com/cn/itunes-u/mathematical-methods-for-engineers/id354869182">iTunes</a> </p>
</li>
<li><p>MIT 15.402 Finance Theory II mainly on Corporate Finance and Pricing <tt>SKIP</tt></p>
</li>
<li><p><a href="https://class.coursera.org/macroeconomics-001/class/index">MOOC Melbourne</a> Principles of Macroeconomics <tt>Skip the Video as the teacher just read lectures </tt></p>
</li>
</ul>
<h2>Links </h2>
<ul>
<li><p><a href="http://ocw.mit.edu/courses/audio-video-courses/">MIT Open Course</a> </p>
</li>
<li><p><a href="https://google-developers.appspot.com/chart/">Google Chart</a> | <a href="http://www.google.com/publicdata/directory">Google Pulice Data</a> | <a href="http://www-958.ibm.com/software/data/cognos/manyeyes/">Many Eyes</a> |<a href="http://www.stamen.com/">Stamen</a> | <a href="http://www.statsoft.com/textbook/">StatSoft Textbook</a></p>
</li>
<li><p><a href="http://cran.r-project.org/web/views/Finance.html">Finance Review</a> |<a href="https://www.rmetrics.org/">RMetrics</a> <a href="http://cran.r-project.org/web/packages/forecast/index.html">Forecast</a> </p>
</li>
</ul>
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