Posts Tagged ‘Video Lectures’

A second post in a series of posts about Information Theory/Learning based perspectives in Evolution, that started off from the last post.

Although the last post was mostly about a historical perspective, it had a section where the main motivation for some work in metabiology due to Chaitin (now published as a book) was reviewed. The starting point about that work was to view evolution solely through an information processing lens (and hence the use of Algorithmic Information Theory). Ofcourse this lens by itself is not a recent acquisition and goes back a few decades (although in hindsight the fact that it goes back just a few decades is very surprising to me at least). To illustrate this I wanted to share some analogies by John Maynard Smith (perhaps one of my favourite scientists), which I had found to be particularly incisive and clear. To avoid clutter, they are shared here instead (note that most of the stuff he talks about is something we study in high school, however the talk is quite good, especially because it tends to emphasize on the centrality of information throughout). I also want this post to act as a reference for some upcoming posts.


Molecular Biology is all about Information. I want to be a little more general than that; the last century, the 19th century was a century in which Science discovered how energy could be transformed from one form to another […] This century will be seen […] where it became clear that information could be translated from one from to another.

[Other parts: Part 2, Part 3, Part 4, Part 5, Part 6]

Throughout this talk he gives wonderful analogies on how information translation underlies the so called Central Dogma of Molecular Biology, and how if the translation was one-way in some stages it could have implications (i.e how August Weismann noted that acquired characters are not inherited by giving a “Chinese telegram translation analogy”; since there was no mechanism to translate acquired traits (acquired information) into the organism so that it could be propagated).

However, the most important point from the talk: One could see evolution as being punctuated by about 6 or so major changes or shifts. Each of these events was marked by the way information was stored and processed in a different way. Some that he talks about are:

1. The origin of replicating molecules.

2. The Evolution of chromosomes: Chromosomes are just strings of the above replicating molecules. The property that they have is that when one of these molecules is replicated, the others have to be as well. The utility of this is the following: Since they are all separate genes, they might have different rates of replication and the gene that replicates fastest will soon outnumber all the others and all the information would be lost. Thus this transition underlies a kind of evolution of cooperation between replicating molecules or in other other words chromosomes are a way for forced cooperation between genes.

3. The Evolution of the Code: That information in the nucleic could be translated to sequences of amino acids i.e. proteins.

4. The Origin of Sex: The evolution of sex is considered an open question. However one argument goes that (details in next or next to next post) the fact that sexual reproduction hastens the acquisition from the environment (as compared to asexual reproduction) explains why it should evolve.

5. The Evolution of multicellular organisms: A large, complex signalling system had to evolve for these different kind of cells to function in an organism properly (like muscle cells or neurons to name some in Humans).

6. Transition from solitary individuals to societies: What made these societies of individuals (ants, humans) possible at all? Say if we stick to humans, this could have only happened only if there was a new way to transmit information from generation to generation – one such possible information transducing machine could be language! Thus giving an additional mechanism to transmit information from one generation to another other than the genetic mechanisms (he compares the genetic code and replication of nucleic acids and the passage of information by language). This momentous event (evolution of language ) itself dependent on genetics. With the evolution of language, other things came by:  Writing, Memes etc. Which might reproduce and self-replicate, mutate and pass on and accelerate the process of evolution. He ends by saying this stage of evolution could perhaps be as profound as the evolution of language itself.


As a side comment: I highly recommend the following interview of John Maynard Smith as well. I rate it higher than the above lecture, although it is sort of unrelated to the topic.


Interesting books to perhaps explore:

1. The Major Transitions in EvolutionJohn Maynard Smith and Eörs Szathmáry.

2. The Evolution of Sex: John Maynard Smith (more on this theme in later blog posts, mostly related to learning and information theory).


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One interesting project that I am involved in these days involves certain problems in Intelligent Tutors. It turns out that perhaps one of the best ways to tackle them is by using Conditional Random Fields (CRFs). Many attempts to solving these problems still involve Hidden Markov Models (HMMs). Since I have never really been a Graphical Models guy (though I am always fascinated) so I found the going on studying CRFs quite difficult. Now that the survey is more or less over, here are my suggestions for beginners to go about learning them.

Tutorials and Theory

1. Log-Linear Models and Conditional Random Fields (Tutorial by Charles Elkan)

Log-linear Models and Conditional Random Fields
Charles Elkan

6 videos: Click on Image above to view

Two directions of approaching CRFs are especially useful to get a good perspective on their use. One of these is considering CRFs as an alternate to Hidden Markov Models (HMMs) while another is to think of CRFs building over Logistic Regression.

This tutorial makes an approach from the second direction and is easily one of the most basic around. Most people interested in CRFs would ofcourse be familiar with ideas of maximum likelihood, logistic regression etc. This tutorial does a good job, starting with the absolute basics – talking about logistic regression (for a two class problem) to a more general multi-label machine learning problem with a structured output (outputs having a structure). I tried reading a few tutorials before this one, but found this to be the most comprehensive and the best place to start. It however seems that there is one lecture missing in this series which (going by the notes) covered more training algorithms.

2. Survey Papers on Relational Learning

These are not really tutorials on CRFs, but talk of sequential learning in general. For beginners, these surveys are useful to clarify the range of problems in which CRFs might be useful while also discussing other methods for the same briefly. I would recommend these two tutorials to help put CRFs in perspective in the broader machine learning sub-area of Relational Learning.

— Machine Learning for Sequential Learning: A Survey (Thomas Dietterich)


This is a very broad survey that talks of sequential learning, defines the problem and some of the most used methods.

— An Introduction to Structured Discriminative Learning (R Memisevic)


This tutorial is like the above, however focuses more on comparing CRFs with large margin methods such as SVM. Giving yet another interesting perspective in placing CRFs.

3. Comprehensive CRF Tutorial (Andrew McCallum and Charles Sutton)


This tutorial is the most compendious tutorial available for CRF. While it claims to start from the bare bone basics, I found it hard for a start and took it on third (after the above two). It is potentially the starting and ending point for a more advanced Graphical Models student. It is extensive (90 pages) and gives a feeling of comfort with CRFs when done. It is definitely the best tutorial available though by no means the most easiest point to start if you have never done any sequential learning before.

This might be considered an extension to this tutorial by McCallum et al : CRFs for Relational Learning (PDF)

4. Original CRF Paper (John Lafferty et al.)


Though not necessary to learn CRFs given many better tutorials, this paper is still recommended, being the first on CRFs.

5. Training/Derivations (Rahul Gupta)


This report is good for the various training methods and for one to go through the derivations associated.

6. Applications to Vision (Nowozin/Lampert)

If your primary focus is using structured prediction in Computer Vision/Image Analysis then a good tutorial (with a large section on CRFs) can be found over here:

Structured prediction and learning in Computer Vision (Foundations and Trends Volume).



Extensions to the CRF concept

There are a number of extensions to CRFs. The two that I have found most helpful in my work are (these are easy to follow given the above):

1. Hidden State Conditional Random Fields (H CRF)

2. Latent Dynamic Conditional Random Fields (LDCRF)

Both of these extensions work to include hidden variables in the CRF framework.


Software Packages

1. Kevin Murphy’s CRF toolbox (MATLAB)

2. MALLET (I haven’t used MALLET, it is Java based)

3. HCRF – LDCRF Library (MATLAB, C++, Python). As as the name suggests, this package is for HCRF and LDCRF, though can be used as a standalone package for CRF as well.

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Here are a number of interesting courses, two of which I am looking at for the past two weeks and that i would hopefully finish by the end of August-September.

Introduction to Neural Networks (MIT):

These days, amongst the other things that I have at hand including a project on content based image retrieval. I have been making it a point to look at a MIT course on Neural Networks. And needless to say, I am getting to learn loads.


I would like to emphasize that though I have implemented a signature verification system using Neural Nets, I am by no means good with them. I can be classified a beginner. The tool that I am more comfortable with are Support Vector Machines.

I have been wanting to know more about them for some years now, but I never really got the time or you can say the opportunity. Now that I can invest some time, I am glad I came across this course. So far I have been able to look at 7 lectures and I should say that I am MORE than very happy with the course. I think it is very detailed and extremely well suited for the beginner as well as the expert.

The instructor is H. Sebastian Seung who is the professor of computational neuroscience at the MIT.

The course has 25 lectures each one packed with a great amount of information. Meaning, the lectures might work slow for those who are not very familiar with this stuff.

The video lectures can be accessed over here. I must admit that i am a little disappointed that these lectures are not available on you-tube. That’s because the downloads are rather large in size. But I found them worth it any way.

The lectures cover the following:

Lecture 1: Classical neurodynamics
Lecture 2: Linear threshold neuron
Lecture 3: Multilayer perceptrons
Lecture 4: Convolutional networks and vision
Lecture 5: Amplification and attenuation
Lecture 6: Lateral inhibition in the retina
Lecture 7: Linear recurrent networks
Lecture 8: Nonlinear global inhibition
Lecture 9: Permitted and forbidden sets
Lecture 10: Lateral excitation and inhibition
Lecture 11: Objectives and optimization
Lecture 12: Excitatory-inhibitory networks
Lecture 13: Associative memory I
Lecture 14: Associative memory II
Lecture 15: Vector quantization and competitive learning
Lecture 16: Principal component analysis
Lecture 17: Models of neural development
Lecture 18: Independent component analysis
Lecture 19: Nonnegative matrix factorization. Delta rule.
Lecture 20: Backpropagation I
Lecture 21: Backpropagation II
Lecture 22: Contrastive Hebbian learning
Lecture 23: Reinforcement Learning I
Lecture 24: Reinforcement Learning II
Lecture 25: Review session

The good thing is that I have formally studied most of the stuff after lecture 13 , but going by the quality of lectures so far (first 7), I would not mind seeing them again.

Quick Links:

Course Home Page.

Course Video Lectures.

Prof H. Sebastian Seung’s Homepage.



This is a Harvard course. I don’t know when I’ll get the time to have a look at this course, but it sure looks extremely interesting. And I am sure a number of people would be interested in having a look at it. It looks like a course that be covered up pretty quickly actually.tornado

[Image Source]

The course description says the following:

The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. The goal of this course is to expose you to visual representation methods and techniques that increase the understanding of complex data. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.

In this course you will learn how the human visual system processes and perceives images, good design practices for visualization, tools for visualization of data from a variety of fields, collecting data from web sites with Python, and programming of interactive visualization applications using Processing.

The topics covered are:

  • Data and Image Models
  • Visual Perception & Cognitive Principles
  • Color Encoding
  • Design Principles of Effective Visualizations
  • Interaction
  • Graphs & Charts
  • Trees and Networks
  • Maps & Google Earth
  • Higher-dimensional Data
  • Unstructured Text and Document Collections
  • Images and Video
  • Scientific Visualization
  • Medical Visualization
  • Social Visualization
  • Visualization & The Arts

Quick Links:

Course Home Page.

Course Syllabus.

Lectures, Slides and other materials.

Video Lectures


Advanced AI Techniques:

This is one course that I would  be looking at some parts of after I have covered the course on Neural Nets.  I am yet to glance at the first lecture or the materials, so i can not say how they would be like. But I sure am expecting a lot from them going by the topics they are covering.

The topics covered in a broad sense are:

  • Bayesian Networks
  • Statistical NLP
  • Reinforcement Learning
  • Bayes Filtering
  • Distributed AI and Multi-Agent systems
  • An Introduction to Game Theory

Quick Link:

Course Home.


Astrophysical Chemistry:

I don’t know if I would be able to squeeze in time for these. But because of my amateurish interest in chemistry (If I were not an electrical engineer, I would have been into Chemistry), and because I have very high regard for Dr Harry Kroto (who is delivering them) I would try and make it a point to have a look at them. I think I’ll skip gym for some days to have a look at them. ;-)


[Nobel Laureate Harry Kroto with a Bucky-Ball model – Image Source : richarddawkins.net]

Quick Links:

Dr Harold Kroto’s Homepage.

Astrophysical Chemistry Lectures


Onionesque Reality Home >>

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In the past month or so I have been looking at a series of lectures on Data Mining that I had long bookmarked. I’ve had a look at the lectures twice and I found them extremely useful, hence I thought it was not a bad idea to share them here, though I am aware that they are pretty old and rather well circulated.

These lectures delivered by Professor David Mease as Google Tech Talks/Stanford Stat202 course lectures, work equally well for beginners as for experts who need to brush up with basic ideas. The course uses R extensively.

data mining icon11Statistical Aspects of Data Mining


Course Video Lectures.

Course website.

Lecture Slides.


I’d end with some Dilbert strips on Data-Mining that I have liked in the past!

Data Mining






Onionesque Reality Home >>

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I recently discovered a series of three lectures by the legendary physicist Hans Bethe given in 1999. Bethe was a professor at  Cornell University almost all his life and these lectures given at age 93 had been made public by the University quite a while ago.


[Hans Bethe at the blackboard at Cornell in 1967: Image Source and Copyright – Cornell University ]

These lectures are on the Quantum theory for expert and the non- expert alike. Due to some engagements I am yet to view them, however I am still posting them as I am sure these as given by Bethe himself would be great.

From the Cornell University Webpage for these lectures:

IN 1999, legendary theoretical physicist Hans Bethe delivered three lectures on quantum theory to his neighbors at the Kendal of Ithaca retirement community (near Cornell University). Given by Professor Bethe at age 93, the lectures are presented here as QuickTime videos synchronized with slides of his talking points and archival material.

Intended for an audience of Professor Bethe’s neighbors at Kendal, the lectures hold appeal for experts and non-experts alike. The presentation makes use of limited mathematics while focusing on the personal and historical perspectives of one of the principal architects of quantum theory whose career in physics spans 75 years.

A video introduction and appreciation are provided by Professor Silvan S. Schweber, the physicist and science historian who is Professor Bethe’s biographer, and Edwin E. Salpeter, the J. G. White Distinguished Professor of Physical Science Emeritus at Cornell, who was a post-doctoral student of Professor Bethe.



View Introduction (Quick Time Required)

The introduction has been given by Edwin E. Salpeter and Silvan S. Schweber.

Lecture 1


View Lecture 1 (Quick Time Required)

Lecture 2


View Lecture 2 (Quick Time Required)

Lecture 3


View Lecture 3 (Quick Time Required)



View Appreciation (Quick Time Required)

Note: All the images above and also the text giving an introduction to the lectures are a copyright of Cornell. Please comply with the terms of use associated with them.


1. Download Apple Quick Time

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Edit (November 05, 2011): Note that this post was made over THREE years ago. That time this was the only comprehensive Machine Learning course available online. Since then situation has changed. Professor Andrew Ng’s course has been offered online for everyone. Many other courses have also become available. Find some of these at the bottom of this post.

Just two weeks ago I posted a few lectures on Machine Learning, Learning Theory, Kernel Methods etc on this post. Since then my friend and guide Sumedh Kulkarni informed me of a new course on the Stanford University youtube channel on Machine Learning I have also indexed this channel on my post on Video Lectures.

Since then I have already seen half of it, and though it covers a very broad range, and is meant to be a first course on Machine Learning, it is in my opinion the best course on the web on the same. Most others I find boring because of either poor English of the instructor or bad recording or both.

The course is taken by Dr Andrew Ng, who has very good experience in teaching this course and working in Robotics, AI and Machine Learning in general. Incidentally he has been the guide of a PhD candidate Ashutosh Saxena, whose research papers we have used for a previous project on pattern recognition.

Dr Ng’s deep knowledge in the field can be felt in just some minutes into the first course which he makes even more interesting by his good communication skills and ability to make lectures more exciting and intuitive by adding fun videos in between.

The course details are as follows.

Course: Machine Learning (CS 229). It can be accessed over here: Stanford Machine Learning.

Instructor: Dr Andrew Ng.

Course Overview:

Lecture 1: Overview of the broad field of Machine Learning.

Lecture 2: Linear regression, Gradient descent, and normal equations and discussion on how they relate to machine learning.

Lecture 3: Locally weighted regression, Probabilistic interpretation and Logistic regression.

Lecture 4: Newton’s method, exponential families, and generalized linear models.

Lecture 5: Generative learning algorithms and Gaussian discriminative analysis.

Lecture 6: Applications of naive Bayes, neural networks, and support vector machine.

Lecture 7: Optimal margin classifiers, KKT conditions, and SUM duals.

Lecture 8: Support vector machines, including soft margin optimization and kernels.

Lecture 9: Learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding’s inequalities.

Lecture 10: VC dimension and model selection.

Lecture 11: Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.

Lecture 12: Unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization.

Lecture 13: Expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.

Lecture 14: Factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).

Lecture 15: Principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning.

Lecture 16: Reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration.

Lecture 17: Reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations.

Lecture 18: State action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs.

Lecture 19: Debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning.

Lecture 20: POMDPs, policy search, and Pegasus in the context of reinforcement learning.

Course Notes: CS 229 Machine Learning.

My gratitude to Stanford and Prof Andrew Ng for providing this wonderful course to the general public.

Other Machine Learning Video Courses:

1. Tom Mitchell’s Machine Learning Course.

Related Posts:

1. Demystifying Support Vector Machines for Beginners. (Papers, Tutorials on Learning Theory, Machine Learing)

2. Video Lectures.

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Though I have worked on pattern recognition in the past I have always wanted to work with Neural Networks for the same. However for some reason or the other I could never do so, I could not even take it as an elective subject due to some constraints. Over the last two years or so I have been promising myself and ordering myself to stick to a schedule and study ANNs properly, however due to a combination of procrastination, over-work and bad planning I have never been able to do anything with them.

However I have now got the opportunity to work with Support Vector Machines and over the past some time I have been reading extensively on the same and have been trying to get playing with them. Now that the actual implementation and work is set to start I am pretty excited to work with them. It is nice that I get to work with SVMs though I could not with ANNs.

Support Vector Machine is a classifier derived from statistical learning theory by Vladimir Vapnik and his co-workers. The foundations for the same were laid by him as late as the 1970s SVM shot to prominence when using pixel maps as input it gave an accuracy comparable with sophisticated Neural Networks with elaborate features in a handwriting recognition task.

Traditionally Neural Networks based approaches have suffered some serious drawbacks, especially with generalization, producing models that can overfit the data. SVMs embodies the structural risk minimization principle that is shown superior to the empirical risk minimization that neural networks use. This difference gives SVMs the greater ability to generalize.

However learning how to work with SVMs can be challenging and somewhat intimidating at first. When i started reading on the topic I took the books by Vapnik on the subject but could not make much head or tail. I could only attain a certain degree of understanding, nothing more. To specialize in something I do well when I start off as a generalist, having a good and quite correct idea of what is exactly going on. Knowing in general what is to be done and what is what, after this initial know-how makes me comfortable I reach the stage of starting with the mathematics which gives profound understanding as anything without mathematics is meaningless. However most books that I came across missed the first point for me, and it was very difficult to make a headstart. There was a book which I could read in two days that helped me get that general picture quite well. I would highly recommend it for most who are in the process of starting with SVMs.The book is titled Support Vector Machines and other Kernel Based Learning methods and is authored by Nello Cristianini and John-Shawe Taylor.

I would highly recommend people who are starting with Support Vector Machines to buy this book. It can  be obtained easily over Amazon.

This book has very less of a Mathematical treatment but it makes clear the ideas involved and this introduces a person studying from it to think more clearly before he/she can refine his/her understanding by reading something heavier mathematically. Another that I would highly recommend is the book Support Vector Machines for Pattern Classification by Shigeo Abe.

Another book that I highly recommend is Learning with Kernels by Bernhard Scholkopf and Alexander Smola. Perfect book for beginners.

Only after one has covered the required stuff from here that I would suggest Vapnik’s books which then would work wonderfully well.

Other than the books there are a number of Video Lectures and tutorials on the Internet that can work as well!

Below is a listing of a large number of good tutorials on the topic. I don’t intend to flood a person interested in starting with too much information, where ever possible i have described what the document carries so that one could decide what should suffice for him/her on the basis of need. Also I have star-marked some of the posts. This marks the ones that i have seen and studied from personally and found them most helpful and i am sure they would work the same way with both beginners and people with reasonable experience alike.

Webcasts/ Video Lectures on Learning Theory, Support Vector Machines and related ideas:

EDIT: For those interested. I had posted about a course on Machine Learning that has been provided by Stanford university. It too is suited for an introduction to Support Vector Machines. Please find the post here. Also this comment might be helpful, suggestions to it according to your learning journey are also welcome.

1. *Machine Learning Workshop, University of California at Berkeley. This series covers most of the basics required. Beginners can skip the sessions on Bayesian models and Manifold Learning.

Workshop Outline:

Session 1: Classification.

Session 2: Regression.

Session 3: Feature Selection

Session 4: Diagnostics

Session 5: Clustering

Session 6: Graphical Models

Session 7: Linear Dimensionality Reduction

Session 8: Manifold Learning and Visualization

Session 9: Structured Classification

Session 10: Reinforcement Learning

Session 11: Non-Parametric Bayesian Models

2. Washington University. Beginners might be interested on the sole talk on the topic of Supervised Learning for Computer Vision Applications or maybe in the talk on Dimensionality Reduction.

3. Reinforcement Learning, Universitat Freiburg.

4. Deep Learning Workshop. Good talks, But I’d say these are meant for only the highly interested.

5. *Introduction to Learning Theory, Olivier Bousquet.

This tutorial focuses on the “larger picture” than on mathematical proofs, it is not restricted to statistical learning theory however. The course comprises of five lectures and is quite good to watch. The Frenchman is both smart and fun!

6. *Statistical Learning Theory, Olivier Bousquet. This course gives a detailed introduction to Learning Theory with a focus on the Classification problem.

Course Outline:

Probabilistic and Concentration inequalities, Union Bounds, Chaining, Measuring the size of a function class, Vapnik Chervonenkis Dimension, Shattering Dimensions and Rademacher averages, Classification with real valued functions.

7. *Statistical Learning Theory, Olivier Bousquet. This is not the repeat of the above course. This one is a more recent lecture series than the above actually. This course has six lectures. Another excellent set.

Course Outline:

Learning Theory: Foundations and Goals

Learning Bounds: Ingredients and Results

Implications: What to conclude from bounds

7. Advanced Statistical Learning Theory, Olivier Bousquet. This set of lectures compliment the above courses on statistical learning theory and give a more detailed exposition of the current advancements in the same.This course has three lectures.

Course Outline:

PAC Bayesian bounds: a simple derivation, comparison with Rademacher averages, Local Rademacher complexity with classification loss, Talagrand’s inequality. Tsybakov noise conditions, Properties of loss functions for classification (influence on approximation and estimation, relationship with noise conditions), Applications to SVM – Estimation and approximation properties, role of eigenvalues of the Gram matrix.

8. *Statistical Learning Theory, John-Shawe Taylor, University of London. One plus point of this course is that is has some good English. Don’t miss this lecture as it has been given by the same professor whose book we just discussed.

9. *Learning with Kernels, Bernhard Scholkopf.

This course covers the basics for Support Vector Machines and related Kernel methods. This course has six lectures.

Course Outline:

Kernel and Feature Spaces, Large Margin Classification, Basic Ideas of Learning Theory, Support Vector Machines, Other Kernel Algorithms.

10. Kernel Methods, Alexander Smola, Australian National University.  This is an advanced course as compared to the above and covers exponential families, density estimation, and conditional estimators such as Gaussian Process classification, regression, and conditional random fields, Moment matching techniques in Hilbert space that can be used to design two-sample tests and independence tests in statistics.

11. *Introduction to Kernel Methods, Bernhard Scholkopf, There are four parts to this course.

Course Outline:

Kernels and Feature Space, Large Margin Classification, Basic Ideas of Learning Theory, Support Vector Machines, Examples of Other Kernel Algorithms.

12. Introduction to Kernel Methods, Partha Niyogi.

13. Introduction to Kernel Methods, Mikhail Belkin, Ohio State University.This lecture is second in part to the above.

14. *Kernel Methods in Statistical Learning, John-Shawe Taylor.

15. *Support Vector Machines, Chih-Jen Lin, National Taiwan University. Easily one of the best talks on SVM. Almost like a run-down tutorial.

Course Outline:

Basic concepts for Support Vector Machines, training and optimization procedures of SVM, Classification and SVM regression.

16. *Kernel Methods and Support Vector Machines, Alexander Smola. A comprehensive six lecture course.

Course Outline:

Introduction of the main ideas of statistical learning theory, Support Vector Machines, Kernel Feature Spaces, An overview of the applications of Kernel Methods.

Additional Courses:

1. Basics of Probability and Statistics for Machine Learning, Mikaela Keller.

This course covers most of the basics that would be required for the above courses. However sometimes the shooting quality is a little shady. This talk seems to be the most popular on the video lectures site, one major reason in my opinion is that the lady delivering the lecture is quite pretty!

2. Some Mathematical Tools for Machine Learning, Chris Burges.

3. Machine Learning Laboratory, S.V.N Vishwanathan.

4. Machine Learning Laboratory, Chrisfried Webers.

Introductory Tutorials (PDF/PS):

1. *Support Vector Machines with Applications (Statistical Science). Click here >>

2. *Support Vector Machines (Marti Hearst, UC Berkeley). Click Here >>

3. *Support Vector Machines- Hype or Hallelujah (K. P. Bennett, RPI). Click Here >>

4. Support Vector Machines and Pattern Recognition (Georgia Tech). Click Here >>

5. An Introduction to Support Vector Machines in Data Mining (Georgia Tech). Click Here >>

6. University of Wisconsin at Madison CS 769 (Zhu). Click Here >>

7. Generalized Support Vector Machines (Mangasarian, University of Wisconsin at Madison). Click Here >>

8. *A Practical Guide to Support Vector Classification (Hsu, Chang, Lin, Via U-Michigan Ann Arbor). Click Here >>

9. *A Tutorial on Support Vector Machines for Pattern Recognition (Christopher J.C Burges, Bell Labs Lucent Technologies, Data mining and knowledge Discovery). Click Here >>

10. Support Vector Clustering (Hur, Horn, Siegelmann, Journal of Machine Learning Research. Via MIT). Click Here >>

11. *What is a Support Vector Machine (Noble, MIT). Click Here >>

12. Notes on PCA, Regularization, Sparisty and Support Vector Machines (Poggio, Girosi, MIT Dept of Brain and Cognitive Sciences). Click Here >>

13. *CS 229 Lecture Notes on Support Vector Machines (Andrew Ng, Stanford University). Click Here >>

Introductory Slides (mostly lecture slides):

1. Support Vector Machines in Machine Learning (Arizona State University). Click here >>

Lecture Outline:

What is Machine Learning, Solving the Quadratic Programs, Three very different approaches, Comparison on medium and large sets.

2. Support Vector Machines (Arizona State University). Click Here >>

Lecture Outline:

The Learning Problem, What do we know about test data, The capacity of a classifier, Shattering, The Hyperplane Classifier, The Kernel Trick, Quadratic Programming.

3. Support Vector Machines, Linear Case (Jieping Ye, Arizona State University). Click Here >>

Lecture Outline:

Linear Classifiers, Maximum Margin Classifier, SVM for Separable data, SVM for non-separable data.

4. Support Vector Machines, Non Linear Case (Jieping Ye, Arizona State University). Click Here >>

Lecture Outline:

Non Linear SVM using basis functions, Non-Linear SVMs using Kernels, SVMs for Multi-class Classification, SVM path, SVM for unbalanced data.

5. Support Vector Machines (Sue Ann Hong, Carnegie Mellon). Click Here >>

6. Support Vector Machines (Carnegie Mellon University Machine Learning 10701/15781). Click Here >>

7. Support Vector Machines and Kernel Methods (CMU). Click Here >>

8. SVM Tutorail (Columbia University). Click Here >>

9. Support Vector Machines (Via U-Maryland at College Park). Click Here >>

10. Support Vector Machines: Algorithms and Applications (MIT OCW). Click Here >>

11. Support Vector Machines (MIT OCW). Click Here >>

Papers/Notes on some basic related ideas (No estoric research papers here):

1. Robust Feature Induction for Support Vector Machines (Arizona State University). Click Here >>

2. Hidden Markov Support Vector Machines (Brown University). Click Here >>

3. *Training Data Set for Support Vector Machines (Brown University). Click Here >>

4. Support Vector Machines are Universally Consistent (Journal Of Complexity). Click Here >>

5. Feature Selection for Classification of Variable Length Multi-Attribute Motions (Li, Khan, Prabhakaran). Click Here >>

6. Selecting Data for Fast Support Vector Machine Training (Wang, Neskovic, Cooper). Click Here >>

7. *Normalization in Support Vector Machines (Caltech). Click Here >>

8. The Support Vector Decomposition Machine (Periera, Gordon, Carnegie Mellon). Click Here >>

9. Semi-Supervised Support Vector Machines (Bennett, Demiriz, RPI). Click Here >>

10. Supervised Clustering with Support Vector Machines (Finley, Joachims, Cornell University). Click Here >>

11. Metric Learning: A Support Vector Approach (Cornell University). Click Here >>

12. Training Linear SVMs in Linear Time (Joachims, Cornell Unversity). Click Here >>

13. *Rule Extraction from Linear Support Vector Machines (Fung, Sandilya, Rao, Siemens Medical Solutions). Click Here >>

14. Support Vector Machines, Reproducing Kernel Hilbert Spaces and Randomizeed GACV (Wahba, University of Wisconsian at Madison). Click Here >>

15. The Mathematics of Learning: Dealing with Data (Poggio, Girosi, AI Lab, MIT). Click Here >>

16. Training Invariant Support Vector Machines (Decoste, Scholkopf, Machine Learning). Click Here >>

*As I have already mentioned above, the star marked courses/lectures/tutorials/papers are the ones that I have seen and studied from personally (and hence can vouch for) and these in my opinion should work best for beginners.

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Going by my convention of posting, this may be considered as an annexe to the previous post. I have been thinking of writing about the open course ware given by the IITs as part of a program that had been conceived quite a few years ago, probably inspired by the MIT OCW.

The IITs ever since their inception since the late 1950s have carved a niche for themselves in technical education. Especially in the under-graduate sphere (i mean to say that is there is no industry or grad school in the world where the IIT under-grads are not at an enviable position ). The IITs have one of the hardest acceptance norms and thus are out of reach for most students, the Ministry of Human Resource development of the Government of India, probably taking a cue out of the leaf of MIT initiated a program to make available to the general student population, the lectures and the courses that are taken in these elite institutes. The IISc also has joined the 7 IITs in making this possible. I read quite a bit about it and realized that it was thought of much earlier than i what i was thinking of it to be!

IIT Delhi
IIT Bombay
IIT Madras
IIT Kanpur
IIT Kharagpur
IIT Guwahati
IIT Roorkee
IISc Bangalore

Click on the images to go to the Institute website.

The Beginning

The NPTEL or theNational Programme for Technology Enhanced Learning was first proposed in 1999 by the Indian Institute of Technology at Madras, soon after a workshop held there in collaboration with the Carnegie Mellon University. Immediately after that all the IITs IIMs and the IISc were involved in a MoU and submitted a proposal on these lines to the ministry of HRD.

The Programme Now

The modified proposals were accepted by the ministry of Human Resource and Development of the Government of India and was funded lavishly. The project was implemented by 2006-2007 and about 110 video courses for full subjects (both at the graduate and undergraduate level) and 129 web based courses were prepared.

I have been noticing everyday that, videos which are part of a course are being uploaded with a very high frequency on youtube and made avialable to all.


It is very likely that such comparisons would be made. However it would be unfair to compare both systems as such. These are basically based on the same principles.

The MIT OCW was a result of the huge efforts undertaken by its former president Dr. Charles M. Vest, the objective was to make courses taught by the best faculty in the world available to the general population of students and teachers without having them to worry about IPR, and to try and inculcate in them a spirit of excellence and promoting among the instructors novel ways of teaching. The MIT OCW has been a phenomenal success.

The IIT OCW as i like calling it, is based on the same ideals, i don’t see it as something against the MIT OCW as i would not shy from referring both ;) when needed. Both serve their purpose well.

I think the MIT OCW has served its purpose very well by both acting as an inspiration and as a catalyst for the rise of such programs which will ultimately benefit the students enormously.

Also, one more thing is that the MIT-OCW offers a very wide range of courses, the courses offered by the IITs as of now would seem to be limited to the technical domain. Some courses at the MIT OCW have a very strong research focus also.


I personally have seen the complete video course on DSP so far and have found it to be very terse. I would say the courses are very well delivered and also that the material is excellent. All of the courses are very well organized in playlists on you-tube and hence are free of all clutter.

As i mentioned earlier that there are 110 video courses and 129 web based courses.

The entire list of the courses along with the conducting institute and the professors associated with the course can be found over here or below:

1. Civil Engineering

2. Computer Science and Engineering

3. Electrical Engineering

4. Electronics and Communications Engineering

6. Mechanical Engineering

7. Ocean Engineering

All of the youtube videos can be found over the NPTEL-HRD Channel.

The channel is divided into playlists for each course, which is constantly being updated with the videos. I like to think that they should be home in another two months time. As around 40 courses have already been uploaded.

NOTE: These lectures are copyrighted to the respective IITs, please do not download them. If you wish to have some series, they can be obtained by post by making a nominal payment also. For further details about this please check this page.


The playlists include the following for instance and can be accessed over here:

1. Aerospace Engineering – Advanced Control Systems Design

2. Aerospace Engineering – Aero-Elasticity

3. Aerospace Engineering – Acoustic Instability in Aerospace Propulsion

4. Aerospace Engineering – Flight Dynamics II (Stability)

5. Aerospace Engineering – Foundations of Scientific Computing

6. Aerospace Engineering – Instability and Transition of Fluid Flows

7. Aerospace Engineering – Introduction to Aerospace Propulsion

8. Aerospace Engineering – Introduction to Helicopter Aerodynamics

9. Aerospace Engineering – Jet Aircraft Propulsion

10. Aerospace Engineering – Optimal Control, Guidance and Estimation

11. Aerospace Engineering – Space Flight Mechanics

12. Aerospace Engineering – Turbomachinary Aerodynamics

13. Biochemical Engineering – Enzyme Science and Engineering

14. Biochemical – Essentials in Immunology

15. Biochemical – Eukaryotic Gene Expressions

16. Biotechnology – Biomathematics

17. Biotechnology – Thermodynamics

18. Chemical Engineering – Advanced Mathematical Techniques in Chemical Engineering

19. Chemical Engineering – Biochemical Engineering

20. Chemical Engineering – Chemical Reaction Engineering

21. Chemical Engineering – Computational Fluid Dynamics

22. Chemical Engineering – Computational Techniques

23. Chemical Engineering – Fundamentals of Transport Processes

24. Chemical Engineering – Fundamentals of Transport Processes- II

25. Chemical Engineering – Heat Transfer

26. Chemical Engineering – Instability and Patterning of Thin Polymer Films

27. Chemical Engineering – Mass Transfer II

28. Chemical Engineering – Modern Instrumental Methods of Analysis

29 Chemical Engineering – Microscale Transport Processes

30. Chemical Engineering – Multiphase Flow

31. Chemical Engineering – Novel Separation Processes

32. Chemical Engineering – Particle Characterization

33. Chemical Engineering – Plant Wide Control of Chemical Processes

34. Chemical Engineering – Process Control and Instrumentation

35. Chemistry – Chemistry of Materials

36. Chemistry – Organic Photochemistry and Pericyclic Reactions

37. Chemistry – Rate Processes

38. Civil – Advanced Hydraulics

39. Civil – Advanced Hydrology

40. Civil – Advanced Structural Analysis

41. Civil – Building Materials and Construction

42. Civil – Design of Reinforced Concrete Structures

43. Civil – Design of Steel Structures

44. Civil – Engineering Geology

45. Civil – Environmental Air Pollution

46. Civil – Finite Element Analysis

47. Civil – Fluid Mechanics

48. Civil – Foundation Engineering

49. Civil – Geosynthesis and Reinforced Soil Structures

50. Civil – Hydraulics

51. Civil – Introduction to Transportation Engineering

52. Civil – Mechanics of Solids

53. Civil – Numerical Methods in Civil Engineering

54. Civil – Modern Surveying Techniques

55. Civil – Prestressed Concrete Structures

56. Civil – Probability Methods in Civil Engineering

57. Civil – Soil Dynamics

58. Civil – Soil Mechanics

59. Civil – Stochastic Hydrology

60. Civil – Stochastic Structural Dynamics

61. Civil – Strength of Materials (SOM)

62. Civil – Structural Analysis II

63. Civil – Structural Dynamics

64. Civil – Surveying

65. Civil – Transportation Engineering – II

66. Civil – Urban Transportation and Planning

67. Civil – Water and Wastewater Management

68. Civil – Water Resources Engineering

69. Civil – Water Resources Systems

70. Civil – Watershed Management

71. Computer Sc – Artificial Intelligence

72. Computer Sc- Artificial Intelligence II

73. Computer Sc- Compiler Design

74. Computer Sc- Computational Geometry

75. Computer Sc- Computer Algorithms II

76. Computer Sc – Computer Architecture

77. Computer Sc – Computer Graphics

78. Computer Sc – Computer Networks

79. Computer Sc – Computer Organization

80. Computer Sc- Cryptography and Network Security

81. Computer Sc – Data Communication

82. Computer Sc – Data Structures and Algorithms

83. Computer Sc – Database Management System

84. Computer Sc – Design and Analysis of Algorithms

85. Computer Sc – Discrete Mathematical Structures

86. Computer Sc- Electronic Design and Automation

87. Computer Sc- Graph Theory

88. Computer Sc- High Performance Computer Architecture

89. Computer Sc- High Performance Computing

90. Computer Sc – Internet Technologies

91. Computer Sc – Introduction to Computer Graphics

92. Computer Sc – Introduction To Problem Solving & Programming

93. Computer Sc – Logic for Computer Science

94. Computer Sc- Low Power VLSI Circuits and Systems

95. Computer Sc- Natural Language Processing

96. Computer Sc- Numerical Optimization

97. Computer Sc- Operating System

98. Computer Sc – Performance Evaluation of Computer Systems

99. Computer Sc- Principles of Programming Languages

100. Computer Sc- Programming and Data Structures

101. Computer Sc- Real Time Systems

102. Computer Sc – Software Engineering

103. Computer Sc – Systems Analysis and Design

104. Computer Sc- Theory of Computation

105. Computer Sc- Theory of Computation (different from above)

106. Design – Ergonomics for Beginners

107. Core Science – Applied Mechanics

108. Core Science – Biochemistry 1

109. Core Science – Classical Physics

110. Core Science – Concept and Evolution of Management Thought

111. Core Science – Engineering Chemistry 1

112. Core Science – Engineering Mechanics

113. Core Science – Engineering Physics II

114. Core Science – Human Resource Management – I

115. Core Science – Leadership

116. Core Science – Management Information System

117. Core Science – Management Science

118. Core Science – Material Science

119. Core Science – Mathematics

120. Core Science – Mathematics II

121. Core Science – Mathematics III

122. Core Science – Numerical Methods and Computation

123. Core Science – Numerical Methods and Computer Programming

124. Core Science – Oscillations and Waves

125. Core Science – Probability and Statistics

126. Core Science – Quantum Mechanics and Applications

127. Core Science – Quantum Physics (Dr V. Balakrishnan, Brilliant Lectures)

128. Core Science – Strategic Management

129. Electrical – Advanced 3G and 4G Wireless Communication

130. Electrical – Advanced Electric Drives

131. Electrical – Advanced Control Systems

132. Electrical – Analog ICs

133. Electrical – Basic Electrical Technology

134. Electrical – Chaos, Fractals, And Dynamical Systems

135. Electrical – Chaos, Fractals, And Dynamical Systems – II

136. Electrical – Circuit Theory

137. Electrical – Communication Engineering

138. Electrical – Control Engineering

139. Electrical – Control Engineering (different from above)

140. Electrical – Digital Integrated Circuits

141. Electrical – Digital Signal Processing

142. Electrical – Dynamics of Physical Systems

143. Electrical – Electrical Machines – I

144. Electrical – Electro Magnetic Fields

145. Electrical – Embedded Systems

146. Electrical – Energy Resources and Technology

147. Electrical – Error Correcting Codes

148. Electrical – Estimation of Signals and Systems

149. Electrical – High Voltage DC Transmission

150. Electrical – Illumination Engineering

151. Electrical – Industrial Automation and Control

152. Electrical – Industrial Drives – Power Electronics

153. Electrical – Industrial Instrumentation

154. Electrical – Information Theory and Coding

155. Electrical – Intelligent Systems and Control

156. Electrical – Introduction to Electronic System Packaging

157. Electrical – Micro and Smart Systems

158. Electrical – Networks and Systems

159. Electrical – Networks, Signals and Systems

160. Electrical – Optimal Control

161. Electrical – Power Electronics

162. Electrical – Power Sys Generation Transmission Distribution

163. Electrical – Power System Analysis

164. Electrical – Power System Dynamics

165. Electrical – Power System Dynamics and Control

166. Electrical – Power System Operations and Control

167. Electrical – RF Integrated Circuits

168. Electrical – Signals and Systems

169. Electronics – Advanced Digital Signal Processing

170. Electronics – Advanced Optical Communication

171. Electronics – Adaptive Signal Processing

172. Electronics – Basic Electronics (Prof Mahanta)

173. Electronics – Basic Electronics

174. Electronics – Broadband Networks

175. Electronics – Circuits for Analog System Design

176. Electronics – Coding Theory

177. Electronics – Design Verification & Test of Digital VLSI Circuits

178. Electronics – Digital Circuits and Systems

179. Electronics – Digital Communication

180. Electronics – Digital Computer Organization

181. Electronics – Digital Image Processing

182. Electronics – Digital Signal Processing

183. Electronics – Digital Systems Design

184. Electronics – Digital VLSI System Design

185. Electronics – Digital Voice and Picture Communication

186. Electronics – Electronics for Analog Signal Processing I

187. Electronics – Electronics for Analog Signal Processing II

188. Electronics – High Speed Devices and Circuits

189. Electronics – MEMS and Microsystems

190. Electronics – Neural Networks and Applications

191. Electronics – Pattern Recognition

192. Electronics – Probability and Random Variables

193. Electronics – Solid State Devices

194. Electronics – Transmission Lines and EM Waves

195. Electronics – VLSI Data Conversion Circuits

196. Electronics – VLSI Design

197. Electronics – Wireless Communication

198. Management – Econometric Modeling

199. Management – Manufacturing Systems Management

200. Management – Operations and Supply Chain Management

201. Management – Operations Management

202. Management – Organization Management

203. Management – Security Analysis and Portfolio Management

204. Management – Six Sigma

205. Management – Strategic Management

206. Material Sciences – Electroceramics

207. Mathematics – Advanced Matrix Theory

208. Mathematics – Applied Multivariate Analysis

209. Mathematics – Calculus of Variations and Integral Equations

210. Mathematics – Elementary Numerical Analysis

211. Mathematics – Functional Analysis

212. Mathematics – Mathematical Logic

213. Mathematics – Measure and Integration

214. Mathematics – Linear Programming and Extensions

215. Mathematics – Real Analysis

216. Mathematics – Regression Analysis

217. Mechanical – Advanced Finite Elements Analysis

218. Mechanical – Advanced Gas Dynamics

219. Mechanical – Advanced Machining Processes

220. Mechanical – Advanced Manufacturing Processes

221. Mechanical – Advanced Materials and Processes

222. Mechanical – Advanced Operations Research

223. Mechanical – Advanced Strength of Materials

224. Mechanical – Basic Thermodynamics

225. Mechanical – Computational Fluid Dynamics

226. Mechanical – Computer Aided Design

227. Mechanical – Computer Aided Engineering Design

228. Mechanical – Computational Methods in Design and Manufacturing

229. Mechanical – Conduction and Radiation

230. Mechanical – Convective Heat and Mass Transfer

231. Mechanical – Cryogenic Engineering

232. Mechanical – Design and Optimization of Energy Systems

233. Mechanical – Design of Machine Elements

234. Mechanical – Dynamics of Machines

235. Mechanical – Engineering Fracture Mechanics

236. Mechanical – Engineering Mechanics

237. Mechanical – Experimental Stress Analysis

238. Mechanical – Finite Element Method

239. Mechanical – Fundamentals of Operation Research

240. Mechanical – Heat and Mass Transfer

241. Mechanical – Fluid Mechanics

242. Mechanical – Industrial Engineering

243. Mechanical – Introduction to the Finite Element Method

244. Mechanical – Kinematics of Machines

245. Mechanical – Mathematical Methods in Engineering and Science

246. Mechanical – Manufacturing Processes I

247. Mechanical – Manufacturing Processes II

248. Mechanical – Mechanical Measurements and Metrology

249. Mechanical – Mechanical Vibrations

250. Mechanical – Principles of Mechanical Measurement

251. Mechanical – Processing of Non-Metals

252. Mechanical – Project and Production Management

253. Mechanical – Refrigeration and Airconditioning

254. Mechanical – Robotics

255. Mechanical – Rocket Propulsion

256. Mechanical – Strength of Materials

257. Mechanical – Tribology

258. Mechanical – Vibration of Structures

259. Metallurgical – Fuels Refactory and Furnaces

260. Metallurgical – Introduction to Biomaterials

261. Metallurgical – Materials and Energy Balance

262. Metallurgical – Non Ferrous Extractive Metallurgy

263. Metallurgical – Physics of Materials

264. Metallurgical – Principles of Physical Metallurgy

265. Metallurgical – Science and Technology of Polymers

266. Metallurgical – Steel Making

267. Mining – Fundamentals of Environmental Pollution and Control

268. Ocean Engineering – Applied Thermodynamics for Marine Systems

269. Ocean Engineering – Elements of Ocean Engineering

270. Ocean Engineering – Health Safety and Environmental Management

271. Ocean Engineering – Hydrostatics and Stability

272. Ocean Engineering – Marine Construction and Welding

273. Ocean Engineering – Marine Hydrodynamics

274. Ocean Engineering – Performance of Marine Vehicles at Sea

275. Ocean Engineering – Seakeeping and Maneuvering

276. Ocean Engineering – Strength and Vibrations of Marine Structures

277. Physics – Astrophysics and Cosmology

278. Physics – Electronics

279. Physics – Nuclear Physics Fundamentals

280. Physics – Quantum Electronics

281. Physics – Quantum Field Theory

282. Physics – Relativistic Quantum Mechanics

283. Physics – Special Topics in Atomic Physics

284. Physics – Special Topics in Classical Mechanics

285. Textiles – Theory of Yarn Structures

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Via DataWrangling, Here is one of my best finds since i took to blogging.

While browsing i came across this post that makes up a comprehensive list of publicly available video lectures on various topics on Physics, Mathematics, Computer Science, Neuro-Science etc.

Peter Skomoroch almost writes my story at the start of the post i made a reference to above. There is just too much to do these days, but i like it.

His blog is also highly recommended. It is one of the best i have come across. Though he writes at a lesser frequency, his posts are very high quality.

Pay a visit here, to find updated links for complete courses.

Here is a complete list of all the videos Peter has compiled:



Computer Science & Engineering

Machine Learning

Neuroscience & Biology

Finance and Econometrics

Seminars, Talks, and Conference Videos:

See http://del.icio.us/pskomoroch/talk+video for more links…



Computer Science & Engineering

Machine Learning

Neuroscience & Biology

Finance and Economics

Open Courseware Directories and Other Video Lecture Roundup Posts

The full post can be viewed here>>

Please note that i have not opened each and every link from the above. If there is any lecture that is not in the public domain, then please notify me. I will remove it (them) with immediate effect. I do not intend to post / propagate stuff which is NOT in the public domain at all.

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