Graphical models Neural network: re-engineering algorithms. The human brain is the most amazing machine learning algorithm ever invented. Support vector mechanism Evaluation
Types of Learning
Inductive LearningGiven examples of fucntion (X, F(X)), where X is the input und F(X) the output. If the outcomeis discrtete. It is
Probality is a special type of regression. probability needs to sum up to 1. Learning Methods
Machine Learning in Practice
The first thing to do is to understand the domain, learn the biology. Learn natural language if you want to process documents. It is also important to understand what the goals are. Sometimes the goals are very clear. We have more things to try than we can. So in sum it is very important to prior understand the domain, the goal and constraints. The next step is the most time consuming. We need to get the actual data. No data, no learning. Big data, big learning and huge opportunities. The more data you have the more crap you have. You have to find your data, you have to clean it and integrate it. You often have multiple sources of data. In all large projects, this is always the case. There is rarely a single source of data, but rather multiple sources of data often with very different origins and you have to put them all together. If you put them all together wrong, then its like garbage in and garbage out. Machine learning is a cycle that has many steps. 6:35 Inductive LearningWe are given a d training example of some unknown function x. The training example is a pair of two things, an input (e.g. text) Simple binary decisions. Appropriate Applications for Supervised Learning
Is changing every day The essence of Inductive LearningIf ther eis no answere to a question Makes discovery 26:00 Tags: ai Edit this page |
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