Neural networks were started about 50 years ago. Their early abilities
were exaggerated, casting doubts on the field as a whole There is a recent renewed interest in the field, however,
because of new techniques and a better theoretical understanding of their capabilities.
Motivation for neural networks:
- Scientists are challenged to use machines more effectively
for tasks currently solved by humans.
- Symbolic Rules don't reflect processes actually used
- Traditional computing excels in many areas, but not
Types of Applications
- Having a computer program itself from a set of examples
so you don't have to program it yourself. This will be a strong focus of this course: neural networks that learn
from a set of examples.
- Optimization: given a set of constraints and a cost
function, how do you find an optimal solution? E.g. traveling salesman problem.
- Classification: grouping patterns into classes: i.e.
handwritten characters into letters.
- Associative memory: recalling a memory based on a partial
- Regression: function mapping
- Modelling higher level reasoning:
- Modelling lower level reasoning:
- audition speech recognition
- speech generation
Neurobiology: Modelling models of how the brain works.
- higher levels: vision, hearing, etc. Overlaps with
- Nonparametric statistical analysis and regression.
- Can human souls/behavior be explained in terms of symbols,
or does it require something lower level, like a neurally based model?
Where are neural networks being used?
- Signal processing: suppress line noise, with adaptive
echo canceling, blind source separation
- Control: e.g. backing up a truck: cab position, rear
position, and match with the dock get converted to steering instructions. Manufacturing plants for controlling
- Siemens successfully uses neural networks for process
automation in basic industries, e.g., in rolling mill control more than 100 neural networks do their job, 24 hours
- Robotics - navigation, vision recognition
- Pattern recognition, i.e. recognizing handwritten characters,
e.g. the current version of Apple's Newton uses a neural net
- Medicine, i.e. storing medical records based on case
- Speech production: reading text aloud (NETtalk)
- Speech recognition
- Vision: face recognition , edge detection, visual search
- Business,e.g.. rules for mortgage decisions are extracted
from past decisions made by experienced evaluators, resulting in a network that has a high level of agreement with
- Financial Applications: time series analysis, stock
- Data Compression: speech signal, image, e.g. faces
- Game Playing: backgammon, chess, go, ...
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