Statistics is probably the most useful course I did as part of my PhD coursework. Statistics is widely applicable to understanding real world information, testing scientific hypothesis, forming models from observed data and so on. Being intrinsic part of the scientific method, it is used in economics, computational search, random algorithms and so on.
In this course, we learnt the techniques of fitting parameters into observed data, and testing hypotheses (method of moments, maximum likelihood estimate and bootstrap methods). Distributions related to normal distribution play an important role in estimating parameters and their variance from actual value.
Our textbook: Mathematical Statistics and Data Analysis (Statistics) by John A. Rice. The book is very well-written and understandable. Technical aspects are "smoothed out" and made accessible and useful to more general audience. Cramster has several solutions to problems from the book. Here is the errata list from the book.
Other Resources
Another course on statistics is here. Read the comments on hypothesis testing here. Lectures on Decision Theory and Bayesian Inference.
SPSS and SAS are widely used statistical packages. Another free statistical tool is the GNU-S, also know as R. An R-tutorial is here.
Saturday, October 25, 2008
Thursday, October 23, 2008
My Faves for Wednesday, October 22, 2008
Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, I.I.T,kharagpur.
Wednesday, October 22, 2008
Course on The Art of Doing Research - CS6281
In this course, we discussed and practised presentation skills (its usually better to emphasize on one aspect more and more, rather than try to cover all aspects of your work), writing research proposals (should be able to show impact and make a convincing argument that we will be able to make the impact), writing paper well (everything that you write should be backed with clear argument), paper organization (introduction should contain all major aspects of the paper, as if it will be the only thing read by most readers) and identifying sources of ideas of a few papers, and other research related topics.
We also read and discussed several papers related to artificial intelligence - first in context of solving one problem of motion planning, and then several individual topics. The aim of reading the papers was not the technical aspects, but the relationship between ideas, what is new in the paper, what is good and bad about the papers and so on. For example, in the research done in motion planning, we read papers on:
- Devising framework that simplifies the problem.
- Uniform sampling for computational efficiency and approximation
- More intelligent sampling
- Computational efficiency of collision detection
- Different approach of sampling: Sensing and utility
- Another approach of sampling: lazy sampling
This gave an overview of how researchers till now approached the problems, and what novel was contributed by the later papers (when many aspects of the problem were already solved).
We also read and discussed several papers related to artificial intelligence - first in context of solving one problem of motion planning, and then several individual topics. The aim of reading the papers was not the technical aspects, but the relationship between ideas, what is new in the paper, what is good and bad about the papers and so on. For example, in the research done in motion planning, we read papers on:
- Devising framework that simplifies the problem.
- Uniform sampling for computational efficiency and approximation
- More intelligent sampling
- Computational efficiency of collision detection
- Different approach of sampling: Sensing and utility
- Another approach of sampling: lazy sampling
This gave an overview of how researchers till now approached the problems, and what novel was contributed by the later papers (when many aspects of the problem were already solved).
Sunday, October 19, 2008
Talk Review: Internet Pioneer Vinton Cerf
Vincent Cerf talks about the future of internet and the world.
1. India and China will have very large internet population and economies by 2035. Fresh water and energy would be more precious than today.
2. Collaboration on the internet creates many new opportunities - like early discovery of epidemics using medical queries made by people on the internet. Real time data will be much more useful and available.
3. People will travel a lot less for office and virtual interactions will be much more real.
4. Much more kinds of devices and individual contributions are enabled by the internet. Devices will be more intelligent.
5. Much more efficient and managable computers will be used in cloud computing. Processes can have unique identifiers which enables them to transfer from one computer to another.
6. Work on interplanetary internet using new protocols is in its initial phase.
1. India and China will have very large internet population and economies by 2035. Fresh water and energy would be more precious than today.
2. Collaboration on the internet creates many new opportunities - like early discovery of epidemics using medical queries made by people on the internet. Real time data will be much more useful and available.
3. People will travel a lot less for office and virtual interactions will be much more real.
4. Much more kinds of devices and individual contributions are enabled by the internet. Devices will be more intelligent.
5. Much more efficient and managable computers will be used in cloud computing. Processes can have unique identifiers which enables them to transfer from one computer to another.
6. Work on interplanetary internet using new protocols is in its initial phase.
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