What is Learning?
Herbert Simon: “Learning is any process by which a system improves performance from experience.”
What is the task?
–Classification
–Categorization/clustering
–Problem solving / planning / control
–Prediction
–others
What is Machine Learning? (Aspect of AI: creates knowledge)
Definition:
“changes in [a] system that … enable [it] to do the same task or tasks drawn from the same population more efficiently and more effectively the next time.” (Simon 1983)
There are two ways that a system can improve:
- By acquiring new knowledge
–acquiring new facts
–acquiring new skills
- By adapting its behavior
–solving problems more accurately
–solving problems more efficiently
Why Study Machine Learning?
Developing Better Computing Systems
- Develop systems that are too difficult/expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck).
- Develop systems that can automatically adapt and customize themselves to individual users.
-
- Personalized news or mail filter
- Personalized tutoring
- Discover new knowledge from large databases (data mining).
- Market basket analysis (e.g., diapers and beer)
- Medical text mining (e.g., migraines to calcium channel blockers to magnesium)
Why is machine learning necessary?
- learning is a hallmark of Intelligence; many would argue that a system that cannot learn is not intelligent.
- without learning, everything is new; a system that cannot learn is not efficient because it rederives each solution and repeatedly makes the same mistakes.
Why is machine learning possible?
It is because there are regularities in the world.
What are Different Varieties of Machine Learning?
- Concept Learning
- Clustering Algorithms
- Connectionist Algorithms
- Genetic Algorithms
- Explanation-based learning
- Transformation-based learning
- Reinforcement learning
- Case-based learning
- Macro learning
- Evaluation Functions
- Cognitive Learning Architectures
- Constructive Induction
- Discovery Systems
- Knowledge capture
What are the types of learning?
- Induction vs. deduction
- Rote Learning (memorization)
- Advice or instructional learning
- Learning by example or practice: Most popular; many applications ==> OCEL.AI
- Learning by analogy; transfer learning
- Discovery Learning
- Others?
What are the types of training? (Many learning methods involve training)
- Supervised learning: uses a series of labeled examples with direct feedback
- Reinforcement Learning: indirect feedback, after many examples
- Unsupervised/clustering learning: no feedback
- Semisupervised
How well the learned system work?
- Generalization
- Performance on unseen or unknown scenarios or data
- Brittle vs. robust performance
- Evaluate performance by testing on data NOT used for testing
- Cross validation methods for small data sets
- The more (relevant) data, the better.
What are the related disciplines?
- Artificial Intelligence
- Data Mining
- Probability and Statistics
- Information theory
- Numerical optimization
- Computational complexity theory
- Control theory (adaptive)
- Psychology (developmental, cognitive)
- Neurobiology
- Linguistics
- Philosophy
References:
- Machine Learning Tom M. Mitchell, McGraw Hill,1997 ISBN: 0-07-042807-7
- Introduction to Machine Learning, N. Nilsson
- Resources:
- http://www.aaai.org/AITopics/html/machine.html
- http://www.ai.univie.ac.at/oefai/ml/ml-resources.html
- http://clgiles.ist.psu.edu/IST511/index.html
Photo by Joshua Sortino on Unsplash
