BibSonomy bookmarks for /user/kirk86https://www.bibsonomy.org/user/kirk86BibSonomy RSS Feed for /user/kirk86Undergraduate Courses | Mathematical Institute Course ManagementMathematics Courses, Undergrad and Postgrad Notes On All Math Subjectshttps://courses.maths.ox.ac.uk/overview/undergraduatekirk862019-11-14T10:14:55+01:00course lecture mathematics notes website <span itemprop="description">Mathematics Courses, Undergrad and Postgrad Notes On All Math Subjects</span>Tutorial on PAC Bayes NIPS 2017Tutorial on PAC Bayes NIPS 2017https://bguedj.github.io/nips2017/pdf/laviolette_nips2017.pdfkirk862019-11-14T21:06:12+01:00bayesian generalization nips2017 notes theory tutorials <span itemprop="description">Tutorial on PAC Bayes NIPS 2017</span>The Modern Algorithmic Toolbox (CS168), Spring 2018-2019The Modern Algorithmic Toolbox By Gregory Valianthttps://web.stanford.edu/class/cs168/index.htmlkirk862019-11-14T10:34:39+01:00course learning lecture notes website <span itemprop="description">The Modern Algorithmic Toolbox By Gregory Valiant</span>The Art of Approximation in Science and Engineering | MIT OpenCourseWareThe Art of Approximation in Science and Engineering | MIT OpenCourseWarehttps://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-055j-the-art-of-approximation-in-science-and-engineering-spring-2008/readings/kirk862019-10-31T13:33:34+01:00approximate book complexity website <span itemprop="description">The Art of Approximation in Science and Engineering | MIT OpenCourseWare</span>Textbook | Calculus Online Textbook | MIT OpenCourseWareTextbook | Calculus Online Textbook | MIT OpenCourseWarehttps://ocw.mit.edu/resources/res-18-001-calculus-online-textbook-spring-2005/textbook/kirk862019-10-31T13:29:15+01:00book calculus website <span itemprop="description">Textbook | Calculus Online Textbook | MIT OpenCourseWare</span>talks.cam : Machine Learning Reading Group @ CUEDMachine Learning Reading Group & Talks @ Cambridgehttp://talks.cam.ac.uk/show/index/6983kirk862019-11-14T17:42:19+01:00lecture notes talks website <span itemprop="description">Machine Learning Reading Group & Talks @ Cambridge</span>Stochastic Processes BookStochastic Processes Book @ Uni Aucklandhttps://www.stat.auckland.ac.nz/~fewster/325/notes/325book.pdfkirk862019-10-31T16:46:47+01:00book probability stats stochastic <span itemprop="description">Stochastic Processes Book @ Uni Auckland</span>Statistical Machine LearningStatistical Machine Learning @ CMU by Ryan Tibshiranihttp://www.stat.cmu.edu/~ryantibs/statml/kirk862019-11-14T20:08:39+01:00course learning lecture notes stats theory <span itemprop="description">Statistical Machine Learning @ CMU by Ryan Tibshirani</span>Stanford Course Notes on MLStanford Course Notes on ML & Recitationshttp://cs229.stanford.edu/section/kirk862019-11-14T11:32:00+01:00course notes website <span itemprop="description">Stanford Course Notes on ML & Recitations</span>STA 621: Nonparametric StatisticsSTA 621: Nonparametric Statisticshttps://web.as.uky.edu/statistics/users/pbreheny/621/F12/kirk862019-11-03T23:47:12+01:00course non-parametric stats website <span itemprop="description">STA 621: Nonparametric Statistics</span>Sociology Graduate Stats I Sociology Graduate Stats I https://www3.nd.edu/~rwilliam/stats1/kirk862019-11-04T13:33:26+01:00course lecture notes stats website <span itemprop="description">Sociology Graduate Stats I </span>Seminar - Fall 2018 Category TheorySeminar - Fall 2018 Category Theoryhttp://math.ucr.edu/home/baez/qg-fall2018/kirk862019-11-15T21:12:15+01:00category-theory lecture mathematics notes website <span itemprop="description">Seminar - Fall 2018 Category Theory</span>Readings | Introduction to Probability and Statistics | Mathematics | MIT OpenCourseWareIntroduction to Probability and Statistics | MIT OpenCourseWarehttps://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/kirk862019-11-14T20:06:10+01:00course lecture notes probability stats website <span itemprop="description">Introduction to Probability and Statistics | MIT OpenCourseWare</span>Properties of Random VariablesProperties of Random Variableshttp://facweb.cs.depaul.edu/sjost/csc423/documents/technical-details/rv-props.pdfkirk862019-10-31T20:11:44+01:00probability stats tutorials <span itemprop="description">Properties of Random Variables</span>Probability NotesProbability Noteshttp://www.columbia.edu/~kr2248/4109/chapter1.pdfkirk862019-11-04T00:18:04+01:00course notes probability <span itemprop="description">Probability Notes</span>Probability and Random Variables | Mathematics | MIT OpenCourseWareThis course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.https://ocw.mit.edu/courses/mathematics/18-440-probability-and-random-variables-spring-2014/kirk862019-11-08T18:30:56+01:00book course lecture website <span itemprop="description">This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.</span>My .emacsSuper amazing dot-emacs!http://home.thep.lu.se/~karlf/emacs.htmlkirk862019-11-10T12:38:58+01:00emacs <span itemprop="description">Super amazing dot-emacs!</span>MIT Statistical Learning Fall 2019MIT Course on Statistical Learning Fall 2019https://www.mit.edu/~9.520/fall19/kirk862019-11-16T01:30:36+01:00course lecture machine-learning notes probability stats website <span itemprop="description">MIT Course on Statistical Learning Fall 2019</span>Matus Telgarsky. Deep Learning Theory ClassDeep Learning Theory Class & Approximation by Matus Telgarskyhttp://mjt.web.engr.illinois.edu/kirk862019-11-14T20:46:39+01:00course deep-learning lecture notes theory website <span itemprop="description">Deep Learning Theory Class & Approximation by Matus Telgarsky</span>Mathematics of InformationMathematics of Information Course Websitehttps://www.mins.ee.ethz.ch/teaching/ha/kirk862019-11-14T21:55:58+01:00course information lecture mathematics notes theory website <span itemprop="description">Mathematics of Information Course Website</span>Mathematics for Computer Science | Electrical Engineering and Computer Science | MIT OpenCourseWareThis subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.Interactive site components can be found on the Unit pages in the left-hand navigational bar, starting with Unit 1: Proofs.https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/kirk862019-11-08T18:31:34+01:00book computation course mathematics proof-systems website <span itemprop="description">This subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.Interactive site components can be found on the Unit pages in the left-hand navigational bar, starting with Unit 1: Proofs.</span>Math 464/564, Spring '07Probability Theory Course at Arizonahttps://www.math.arizona.edu/~tgk/464_07/kirk862019-11-14T20:09:46+01:00course lecture notes probability theory website <span itemprop="description">Probability Theory Course at Arizona</span>Marc Toussaint - Optimization Course SS 13 U StuttgartMarc Toussaint - Optimization Course SS 13 U Stuttgarthttps://ipvs.informatik.uni-stuttgart.de/mlr/marc/teaching/13-Optimization/kirk862019-11-14T17:31:52+01:00course lecture notes optimization <span itemprop="description">Marc Toussaint - Optimization Course SS 13 U Stuttgart</span>MAE 280A - Fall 2014 Linear Systems Fall 2014 Linear Systems Coursehttp://control.ucsd.edu/mauricio/courses/mae280a/kirk862019-11-14T17:25:55+01:00course lecture mathematics notes symmetry <span itemprop="description">Fall 2014 Linear Systems Course</span>Luca Trevisan Lecture Notes on Theoretical CS and MLLuca Trevisan Lecture Notes on Theoretical CS, ML and Optimhttps://lucatrevisan.github.io/kirk862019-11-14T20:16:45+01:00complexity computation course lecture notes optimization probability stats theory website <span itemprop="description">Luca Trevisan Lecture Notes on Theoretical CS, ML and Optim</span>Lecture Notes | in theoryThis page provides quick links to lecture notes that I have written for various classes: CS254: A graduate class on computational complexity (Stanford) [Spring 2010 Class Home Page] [Notes for Lectures 1-8] CS278: A graduate class on computational complexity (Berkeley) [Spring 2001 Class Home Page] [Fall 2002 Class Home Page] [2001 Lecture Notes in book…https://lucatrevisan.wordpress.com/lecture-notes/kirk862019-11-09T20:39:24+01:00complexity computation course lecture notes optimization theory website <span itemprop="description">This page provides quick links to lecture notes that I have written for various classes: CS254: A graduate class on computational complexity (Stanford) [Spring 2010 Class Home Page] [Notes for Lectures 1-8] CS278: A graduate class on computational complexity (Berkeley) [Spring 2001 Class Home Page] [Fall 2002 Class Home Page] [2001 Lecture Notes in book…</span>Introduction to the BootstrapIntroduction to the Bootstrap, Stats Theoryhttp://statweb.stanford.edu/~susan/courses/s208/web1.htmlkirk862019-11-03T17:17:16+01:00course stats theory tutorials website <span itemprop="description">Introduction to the Bootstrap, Stats Theory</span>ICML 2011, The 28th International Conference on Machine Learning - Bellevue, WashingtonTutorials on Bandits and Kernelshttp://www.icml-2011.org/tutorials.phpkirk862019-11-13T12:29:28+01:00icml2011 tutorials website <span itemprop="description">Tutorials on Bandits and Kernels</span>Gaussian Processes: From the Basics to the State-of-the-ArtGaussian Processes: From the Basics to the State-of-the-Arthttp://cbl.eng.cam.ac.uk/pub/Public/Turner/News/imperial-gp-tutorial.pdfkirk862019-11-14T17:47:05+01:00gaussian-proceses tutorials <span itemprop="description">Gaussian Processes: From the Basics to the State-of-the-Art</span>Foundations of Probability BookFoundations of Probability Bookhttps://www.stat.auckland.ac.nz/~fewster/325/210book.pdfkirk862019-10-31T16:26:13+01:00book probability stats <span itemprop="description">Foundations of Probability Book</span>Emacs Tips and Tricks by Gurmeet Singh MankuEmacs Tips and Tricks by Gurmeet Singh Mankuhttp://xenon.stanford.edu/~manku/dotemacs.htmlkirk862019-11-14T20:00:57+01:00emacs <span itemprop="description">Emacs Tips and Tricks by Gurmeet Singh Manku</span>Emacs ConfigEmacs Config dotfileshttps://github.com/c02y/dotemacs.dkirk862019-11-14T20:01:44+01:00emacs <span itemprop="description">Emacs Config dotfiles</span>Electromagnetics and Applications | Electrical Engineering and Computer Science | MIT OpenCourseWareThis course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-013-electromagnetics-and-applications-spring-2009/kirk862019-11-08T18:35:15+01:00book course physics website <span itemprop="description">This course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.</span>Electromagnetic Fields, Forces, and Motion | Electrical Engineering and Computer Science | MIT OpenCourseWareThis course examines electric and magnetic quasistatic forms of Maxwell's equations applied to dielectric, conduction, and magnetization boundary value problems. Topics covered include: electromagnetic forces, force densities, and stress tensors, including magnetization and polarization; thermodynamics of electromagnetic fields, equations of motion, and energy conservation; applications to synchronous, induction, and commutator machines; sensors and transducers; microelectromechanical systems; propagation and stability of electromechanical waves; and charge transport phenomena. Acknowledgments The instructor would like to thank Thomas Larsen and Matthew Pegler for transcribing into LaTeX the homework problems, homework solutions, and exam solutions.https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-641-electromagnetic-fields-forces-and-motion-spring-2009/kirk862019-11-08T18:35:32+01:00book course physics website <span itemprop="description">This course examines electric and magnetic quasistatic forms of Maxwell's equations applied to dielectric, conduction, and magnetization boundary value problems. Topics covered include: electromagnetic forces, force densities, and stress tensors, including magnetization and polarization; thermodynamics of electromagnetic fields, equations of motion, and energy conservation; applications to synchronous, induction, and commutator machines; sensors and transducers; microelectromechanical systems; propagation and stability of electromechanical waves; and charge transport phenomena. Acknowledgments The instructor would like to thank Thomas Larsen and Matthew Pegler for transcribing into LaTeX the homework problems, homework solutions, and exam solutions.</span>EE 227C (Spring 2018) Convex Optimization and ApproximationEE227C (Spring 2018) Course page on Convex Optimization and Approximationhttps://ee227c.github.io/kirk862019-11-05T03:18:44+01:00approximate convex course notes optimization tutorials website <span itemprop="description">EE227C (Spring 2018) Course page on Convex Optimization and Approximation</span>Deep Learning in Computer VisionDeep Learning in Computer Vision Coursehttp://www.scs.ryerson.ca/~kosta/CP8309-F2018/index.htmlkirk862019-11-14T20:04:18+01:00course deep-learning lecture notes theory website <span itemprop="description">Deep Learning in Computer Vision Course</span>Deep Learning for Computer Vision Barcelona | 2016Deep Learning for Computer Vision Barcelona 2016http://imatge-upc.github.io/telecombcn-2016-dlcv/kirk862019-11-14T17:54:03+01:00course deep-learning lecture notes summer-schools theory website <span itemprop="description">Deep Learning for Computer Vision Barcelona 2016</span>CSE 291 Fall 2012: Learning TheoryFall 2012: Learning Theory Course Materialhttps://cseweb.ucsd.edu/classes/fa12/cse291-b/kirk862019-11-14T13:44:23+01:00course learning lecture notes stats theory website <span itemprop="description">Fall 2012: Learning Theory Course Material</span>CS 70 | Discrete Math and ProbabilityDiscrete mathematics and probability coursehttp://www.eecs70.org/kirk862019-11-14T20:19:16+01:00course lecture mathematics notes probability website <span itemprop="description">Discrete mathematics and probability course</span>Create an epsilon of room | TrickiTricks and Tips solving math problemshttp://www.tricki.org/article/Create_an_epsilon_of_roomkirk862019-11-14T20:20:49+01:00blog mathematics tutorials website <span itemprop="description">Tricks and Tips solving math problems</span>Computer Science 294: Practical Machine LearningComputer Science 294: Practical Machine Learning by Michael Jordanhttps://people.eecs.berkeley.edu/~jordan/courses/pml/kirk862019-11-14T17:17:04+01:00course lecture machine-learning notes optimization stats theory website <span itemprop="description">Computer Science 294: Practical Machine Learning by Michael Jordan</span>Classical Mechanics | MIT OpenCourseWareClassical Mechanics | MIT OpenCourseWarehttps://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/syllabus/kirk862019-10-31T13:31:36+01:00book physics website <span itemprop="description">Classical Mechanics | MIT OpenCourseWare</span>Cambridge Machine Learning Group | HomepageCambridge Machine Learning Group & Course Resourceshttp://mlg.eng.cam.ac.uk/kirk862019-11-14T17:46:34+01:00course lecture notes website <span itemprop="description">Cambridge Machine Learning Group & Course Resources</span>Blog About NLPBlog About NLPhttps://lena-voita.github.io/posts.htmlkirk862019-11-14T17:48:40+01:00blog nlp <span itemprop="description">Blog About NLP</span>Bayesian data analysis & cognitive modeling 2015Bayesian data analysis & cognitive modeling 2015http://www.sfs.uni-tuebingen.de/~mfranke/bda+cm2015/kirk862019-11-14T14:20:35+01:00bayesian course lecture notes stats website <span itemprop="description">Bayesian data analysis & cognitive modeling 2015</span>Arthur Gretton | Advanced Topics in Machine Learning CourseAdvanced Topics in Machine Learning Course by Arthur Gretton
http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/http://www.gatsby.ucl.ac.uk/~gretton/teaching.htmlkirk862019-11-14T18:13:59+01:00course lecture machine-learning notes website <span itemprop="description">Advanced Topics in Machine Learning Course by Arthur Gretton
http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/</span>Anatoli Juditsky's page - LecturesStatistical Theory and Convex Optimisationhttps://sites.google.com/view/anatoli-juditsky/lectures?authuser=0kirk862019-11-14T18:00:13+01:00lecture notes optimization stats theory website <span itemprop="description">Statistical Theory and Convex Optimisation</span>Algorithmic Foundations of Learning 2018 - Oxford UniversityAlgorithmic Foundations of Learning 2018 - Oxford Universityhttp://www.stats.ox.ac.uk/~rebeschi/teaching/AFoL/18/kirk862019-11-14T17:56:15+01:00course learning lecture notes theory website <span itemprop="description">Algorithmic Foundations of Learning 2018 - Oxford University</span>A Primer on PAC-Bayesian LearningA Primer on PAC-Bayesian Learning Tutorialhttps://bguedj.github.io/icml2019/material/main.pdfkirk862019-11-14T21:05:08+01:00bayesian generalization icml2019 lecture notes tutorials <span itemprop="description">A Primer on PAC-Bayesian Learning Tutorial</span>18.S996S13 Textbook: Category Theory For ScientistsTextbook: Category Theory For Scientistshttps://ocw.mit.edu/courses/mathematics/18-s996-category-theory-for-scientists-spring-2013/textbook/MIT18_S996S13_textbook.pdfkirk862019-10-31T13:27:17+01:00book category-theory <span itemprop="description">Textbook: Category Theory For Scientists</span>