Statistical binary classification is a problem studied in machine learning . It is a type of supervised learning , a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories.
The idea behind perceptrons (the predecessors to artificial neurons) is that it is possible to mimic certain parts of neurons, such as dendrites, cell bodies and axons using simplified mathematical models of what limited knowledge we have on their inner workings:
Signals can be received from dendrites, and sent down the axon once enough signals were received.
This outgoing signal can then be used as another input for other neurons, repeating the process. Some signals are more important than others and can trigger some neurons to fire easier.
Connections can become stronger or weaker, new connections can appear while others can cease to exist.
Even though artificial intelligence was inspired by our own, the advancements in the field in return help biologists and psychologists to better understand intelligence and evolution.
What do voice search, machine translation, and ATMs have in common? Learn the answer to that question and more
Here is how to write your own FIRST Simple Machine learning application using Python code! If you are interested just in Python? Here is my project in Bioinformatics : Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view Learn more here..
:: Question asked ::
(From UCLA's Deep Cytometry)
" What is cytometry? "
Cytometry is the measurement of the characteristics of cells.
Variables that can be measured by cytometric methods include cell size, cell count, cell morphology (shape and structure), cell cycle phase, DNA content, and the existence or absence of specific proteins on the cell surface or in the cytoplasm.
SethBling, a competitive gamer became interested in artificial intelligence and machine learning.
He married his two interests and came up with MarI/O, an artificial intelligence program that taught itself how to successfully complete the first level of Super Mario World.
1 of 7 :: statistical binary classification ( a machine learning study )
2 of 7 :: Geoff Hinton Godfather of A.I
3 of 7 :: How does your phone know that this is a dog?
4 of 7 :: living in the age of A.I
5 of 7 :: unravel the dynamics of cause and effect ( in genetics & cell biology )
6 of 7 :: Machine learning using python
7 of 7 :: Playing Mario with Deep Reinforcement Learning
This program takes in input, makes it’s own decision and produces an output, then learns from the reward yielded by the output, and repeats the entire process over and over again.
Usually, when we get AI systems to watch video games, we expect them to play the games afterward. That’s how computers have beaten everything from the board game Go to various Atari titles. But a group of researchers from the Georgia Institute of Technology are trying something different: they’re getting AI to learn how video games work instead: