INTERESTING LINKS
Here I list out some interesting links to lecture notes/video lectures of different subjects that I have come across during the time I have been on the journey to discover knowledge.
Also check out relevant courses at Coursera, EdX and Udacity. Any suggestions on more resources, mail me or tweet me.
Machine Learning
Probability and StatisticsOther Links
Also check out relevant courses at Coursera, EdX and Udacity. Any suggestions on more resources, mail me or tweet me.
Machine Learning
- Andrew Ng's ML @ Stanford
- Graphical Models
- Statistical Data Mining Tutorials
- Courses Offered by Michael I Jordan @ UC Berkeley
- ML CMU 1 by Roni - Little advanced
- ML CMU 2- Another nice set, more for beginners
- ML Summer School Video Lectures
- Machine Learning and Probabilistic Graphical Models - Good Slides
- Data Mining - Good Slides
- Introduction to Pattern Recognition - Good Slides
- Advanced AI Wisconsin 2011
- Machine Learning by Eric Xing CMU 2012
- Learning from Data Caltech
- Noah's ARK- A research group on NLP
- CMU/Language Technologies Institute:Research
- The Stanford NLP (Natural Language Processing) Group
- University of Edinburgh NLP Courses
- Applied NLP
- NLP SUNY Buffalo
- NLP Illinois
- NLP Stanford
- NLP Umass
- NLP cornell
- NLP Martin's Slides
- NLP Jurafsky
- NLP Berkeley
- Introduction to Information Retrieval
- Computational Linguistics
- CSE515 Statistical Methods in Computer Science – Spring 2011
- Advanced NLP: Bayesian Methods in NLP - Illinois
- NLP Stonybrook
- IR Resources - (Saved my effort to search for resources...)
- Agnar's fag
- Case-Based Reasoning at AIAI
- K-State KDD Lab: AI-CBR
- AITopics / CaseBasedReasoning
- Cbrwiki
- CBR at Auckland
- 15-852 RANDOMIZED ALGORITHMS
- CS 174: Randomized Algorithms
- Approximation Algorithms at UIUC
- Lecture-notes: Approximation Algorithms for Network Problems
- CS170: EFFICIENT ALGORITHMS & INTRACTABLE PROBLEMS, FALL 2006
- Advanced Approximation Algorithms, Spring 2008
- Prerequisite for the courses - Intro to CS Theory
- Machine learning course notes
- Kernels ICI
- University of Toronto's course, Fall 2009
- Topics in Machine Learning (TIML-09)
- Bayesian Reasoning and Machine Learning - Excellent Book
- Bayesian Networks without tears- by Eugene Charniak
- Caltech CS/CNS/EE 155 Probabilistic Graphical Models
- Univ of Washington: Graphical Models
- A Short Course on Graphical Models
- CS532c Fall 2004 (Topics in AI: probabilistic graphical models)
- Topics in Multivariate Analysis: PGM
- EE512 - Graphical Models
- Convex Optimization by Stephen Boyd
- Convex Optimization by L. Vandenberghe
- Optimization at CMU
- Optimization Course at UPenn
- Saketha Nath's course at IITB
- Computer Organization Class Notes- Very intuitive
- Computer Organization
- Computer Architecture and Organization
- CS 332: Algorithms (Good set of slides for those following Cormen)
- Lecture Slides for Algorithm Design by Jon Kleinberg and Éva Tardos
- Red/Black Tree Demo
- Discrete Maths by Shai Simonson (Really Good)
- Theory of Computation by Shai Simonson (Really Good)
- Algorithms by Shai Simonson
- Jeff Erikson's Algorithms (He has style n substance)
Probability and StatisticsOther Links