Recently I’ve started to see a lot of questions regarding to the “mathematics tutorials or supplementary materials for Machine Learning and AI” in the online discussions with the emergence of Stanford’s online AI and machine learning courses. As with the internet crowd, I’m going to participate these courses as well and I’ve always found the [...]
Archive for the ‘Artificial Intelligence’ Category
Online Supplementary Mathematics Materials for Machine Learning and Artificial Intelligence Courses
04 Oct 2011 at 08:48
caglar
Artificial Intelligence, Computer Science, Engineering, Machine Learning, Mathematics, Web
Large Scale Bayesian Inference for Network Tomography
02 Jun 2011 at 21:41
caglar
Artificial Intelligence, Computer Science, Machine Learning, Mathematics, Network Engineering, statistics, Web
1 Introduction Major goal of network tomography is to infer the internal characteristics of network by only using data from the end nodes. Each node can either be a computer, router or a subnetwork. Broadly speaking large-scale network inference involves estimating network parameters (can be performance or other) based on traffic measurements at a limited subset of nodes [...]
OpenClassroom and E-learning
26 Mar 2011 at 20:46
caglar
Artificial Intelligence, Computer Science, Education, Machine Learning, Technology, Web
Recently I’ve discovered a very useful gem OpenClassroom from Stanford University. In that page there are lectures from several famous professors in Stanford University. The lectures are usually interactive and visual, for instance lecturer asks a question and the videos stops, then it continues after you’ve answered the question. I believe in free online education [...]
Importance Sampling
19 Jan 2011 at 16:24
caglar
Artificial Intelligence, Computer Science, Engineering, Machine Learning, Mathematics, Programming, science, statistics
Importance sampling is probably one of the easiest sampling algorithm and one of the most fundamental one as well. The main purpose of it is to estimate the properties of a particular distribution, while only having samples generated from a different distribution rather than the distribution of interest. Depending on the application, the term may [...]
Probability and Statistics Cheat sheet
Previously I’ve mentioned about a math/cs cheat sheet. It was pretty useful for me. But today I’ve found another interesting cheat sheet on the internet while searching for a definitely unrelated thing (convex optimization, if you really wonder and I found on John D. Cook‘s blog). This cheat sheet definitely extends the definition of a classical cheat [...]
Hypercomputation
20 Dec 2010 at 21:36
caglar
Artificial Intelligence, complexity, Computer Science, Philosophy, science
You think that anything computable can be computed with a Turing machine. Now forget about Church-Turing Thesis for a while and ladies and gentlemen, here comes the HyperComputers!: Christof Teuscher et al, 2002, Hypercomputation: hype or computation? Copeland and Proudfoo, Alan Turing’s Forgotten ideas, 1999, Scientific American Selim G. Akl, The Myth of Universal computation, [...]
Should implementing ML algorithms banned for Production Systems?
19 Dec 2010 at 17:51
caglar
Artificial Intelligence, Computer Science, Engineering, Machine Learning, Softwares
Nowadays everybody is talking about the how machine-learning algorithms can be useful your business, but now I’ll discuss here how it can harm your business . As a design principle(best practice), for the sake of security-preservation and efficiency in cryptographic systems, implementation of cryptographic algorithms isn’t recommended for production systems when there is already [...]
Measure Kolmogorov Complexity of a file with the Lazy Man’s technique in *nix
06 Dec 2010 at 20:51
caglar
Artificial Intelligence, complexity, Computer Science, Engineering, Linux, Systems, Web
With the gnu ent command you get the entropy of a file in an easy way. For example: 1 2 3 4 5 6 7 8 9 10 11 12 13 caglar@caglar-desktop:/tmp$ cat /dev/urandom | base64 | head -c 1200 > rand.txt caglar@caglar-desktop:/tmp$ ent rand.txt Entropy = 5.982286 bits per byte. Optimum compression would [...]
Natural Language Programming For Working Developer
30 Nov 2010 at 21:30
caglar
Artificial Intelligence, Cognitive Science, Computer Science, Language Science, Technology
The title explains all the buzz. If you are seeking for a short, brief but an informative introduction to NLP. The following web site is suitable for you. Also that site explains the fundamentals of Haskell in a very approachable way. Link: Natural Language Programming For the Working Programmer Related Posts:Learn you a haskell for [...]
A reading list for Bayesian techniques
30 Nov 2010 at 10:32
caglar
Artificial Intelligence, Cognitive Science, Computer Science, literature, Machine Learning, science, statistics
Computational Cognitive Science Lab of Univ. of California Berkeley has a very nice reading-list for bayesian methods that are used in Machine-learning, statistics and Cognitive Science: A reading List for Bayesian Methods Related Posts:Rejection Letters from Peer ReviewersPapers that Everyone should read about Computer Science and Mathematics
Recent Comments