Archive for the ‘machine learning’ tag
It is hard to resist a project called ‘Sam I Am’.
Sam I Am (Sensitivity Analysis Modeling Inference and More) provides GUI Java tools for designing and experimenting with Bayesian Networks.
They do not appear to be open source, but they are free for you to download and use.
SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA.
Samiam includes two main components: a graphical user interface and a reasoning engine. The graphical interface lets users develop Bayesian network models and save them in a variety of formats. The reasoning engine supports many tasks including: classical inference; parameter estimation; time-space tradeoffs; sensitivity analysis; and explanation-generation based on MAP and MPE.
Human Computer Interfaces
Probabilistic Graphical Models
Computer Science 101
Software as a Service
Natural Language Processing
Design and Analysis of Algorithms I
It’s my thought that the future of education will not be 4 year degrees on site, but life long learning at home.
The sight of a cockroach scurrying for cover may be nauseating, but the insect is also a biological and engineering marvel, and is providing researchers at Oregon State University with what they call “bioinspiration” in a quest to build the world’s first legged robot that is capable of running effortlessly over rough terrain.
If the engineers succeed, they may owe their success to what’s being learned from these insects and other animals, such as the guinea hen, that have their own remarkable abilities.
. . .
Within certain limitations, Schmitt said, cockroaches don’t even have to think about running – they just do it, with muscle action that is instinctive and doesn’t require reflex control. That, in fact, is part of what the engineers are trying to achieve. Right now some robots have been built that can walk, but none of them can run as well as their animal counterparts. Even walking robots absorb far too much energy and computing power to be very useful.
“If we ever develop robots that can really run over rough ground, they can’t afford to use so much of their computing abilities and energy demand to accomplish it,” Schmitt said. “A cockroach doesn’t think much about running, it just runs. And it only slows down about 20 percent when going over blocks that are three times higher than its hips. That’s just remarkable, and an indication that their stability has to do with how they are built, rather than how they react.”
. . .
In a computer model, they’ve created a concept that would allow a running robot to recover from a change in ground surface almost as well as a guinea hen. They are studying how the interplay of concepts such as energy storage and expenditure, sensor and feedback requirements, and leg angles can produce recovery from such perturbations. Ultimately, a team of OSU engineers hopes to use knowledge such as this to actually build robots that can efficiently run over rough terrain without using significant computing power.
Training a Large Scale Classifier with the Quantum Adiabatic Algorithm
Qubuit.org, Center for Quantum Computing
Introduction to Quantum Computing
The Quantum Computer
Quantum Computing and Shor’s Algorithm
Quantum Computing Day 1, Google Tech Talk on YouTube
Quantum Computing Day 2, Google Tech Talk on YouTube
Quantum Computing Day 3, Google Tech Talk on YouTube
We’ve all been to various .edu sites to the engineering courses only to find a Power Point Presentation with little information.
You have access to video of all the lectures, transcripts of all the lectures and class notes. I started working through it last week, brushing up on old topics and filling in gaps in my knowledge. Of all the online artificial intelligence and machine learning courses online, this is the best I’ve seen so far. The math is very low level, programming minimal, you’ll find it a very easy to work through introduction to machine learning.
Artificial Intelligence | Machine Learning
Instructor: Ng, Andrew
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background:
Prerequisites: – Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
– Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
– Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
MIT also has a class available for Machine Learning, no lectures, but there are pdf notes.