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 by humans
• Traditional computing excels in many areas, but not in others. 

 

Types of Applications

 

Machine learning:

• 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 match.
• Regression: function mapping

Cognitive science:

• Modelling higher level reasoning:
  • language
  • problem solving
• Modelling lower level reasoning:
  • vision
  • audition speech recognition
  • speech generation

Neurobiology: Modelling models of how the brain works.

• neuron-level
• higher levels: vision, hearing, etc. Overlaps with cognitive folks.

Mathematics:

• Nonparametric statistical analysis and regression.

Philosophy:

• 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 automated machines.
• 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 a day
• 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 information
• Speech production: reading text aloud (NETtalk)
• Speech recognition
• Vision: face recognition , edge detection, visual search engines
• 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 human experts.
• Financial Applications: time series analysis, stock market prediction
• Data Compression: speech signal, image, e.g. faces
• Game Playing: backgammon, chess, go, ...