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 ‘Mathematics’ 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
Basic Concepts in Functional Programming Part 1
In a series of posts I’ll try to summarize (only briefly) the basic concepts of functional programming. Functional Programming According to wikipedia the definition of functional programming is: In computer science, functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It emphasizes the [...]
Philosophy and Computer Science
I’ve just found out an interesting MIT course on Philosophy and Theoretical Computer Science with code 6.893 and thought by Scott Aaronson. The most attractive part of this course for me was articles included in the reading list of the web page. There are great articles in that list: Scott Aaronson, NP-complete Problems and Physical Reality [...]
Learn you a haskell for Math! – Part 1
It has been for some time since I’ve used haskell for programming (in fact for my personal and work related projects I don’t use haskell. because I still feel so novice at it), so I thought that implementing some basic mathematical functions might be a good exercise. I’m planning to write 3 series, in the [...]
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 [...]
Math behind the Quantum Computers
AMS has excellent math articles for introductory and general audience in a series of feature columns every month. Luckily those articles are accessible from the web. Recently I noticed two very interesting articles on mathematics behind the quantum computers. The articles can be accessed from those links part 1 and part 2. The math described [...]
Matlab Commands in NumPy and R
There is a very nice pdf prepared by V. B. Gunderson for the correspondents of Matlab commands in numpy and R. You can access the pdf version of document by clicking here. Take note this site, one day you may need it . Related Posts:No Related Posts
A Basic Math Mind Map
Mind maps are usually useful for visualizing and organizing ideas in an organic way. It is very useful for brainstorming and learning new concepts. There are several nice mind maps on several different concepts, one of them that you may find useful if you are trying to refresh your fundamental math knowledge is that one: [...]
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 [...]
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