AI Week 22 Machine Learning Data Mining Lee McCluskey, room 2/07

Artform Research Group Overview Intelligent (virtual) Agents are defined as.. Software programs capable of autonomous behaviour in dynamic unpredictable environments THAT IS – THEY CAN u PLAN to solve goals u Execute plans u Communicate/co-operate u Sense/Observe/Gather information u ADAPT / LEARN

Artform Research Group Learning “a [positive] change of behaviour” Learning occurs in various ways (simplest first): n Learning by ROTE (remember facts) n Learning by BEING TOLD (programmed) n Learning by EXAMPLE/ANALOGY (trained/taught) n Learning by OBSERVATION (self-taught) n Learning by DISCOVERY (invent)

Artform Research Group Types of Learning Learning by ROTE (remember facts) - this is purely storing and remembering facts without integrating or recognising the meaning of the facts Learning by BEING TOLD (programmed) - this is storing and remembering facts / procedures, but implies some kind of understanding / integration of what is being told, with previous knowledge. Learning by EXAMPLE/ANALOGY (trained/taught) this invovles a benevolent teacher who gives classified examples to the leaner. The learner performs some generalisation the examples to infer new knowledge. Previous knowledge maybe used to steer the generalisations. In analogy the learner performs the generalisation based on some previously learnt situation.

Artform Research Group Types of Learning Learning by OBSERVATION (self-taught) this is similar to the category above but without classification by teacher - the learner uses pre-learned information to help classify observation (eg conceptual clustering) Learning by DISCOVERY this is the highest level of learning covering invention etc and is composed of some of the other types below TWO ASPECTS OF LEARNING: KNOWLEDGE/SKILL ACQUISITION Inputting NEW knowledge KNOWLEDGE/SKILL REFINEMENT Changing/integrating old knowledge to create better (operational) knowledge (Inputs no or little new knowledge)

Artform Research Group KNOWLEDGE/SKILL REFINEMENT n Changing/integrating old knowledge to create better (operational) knowledge (Inputs no or little new knowledge) Examples: - Learning heuristics (improve search) - Re-representing knowledge (improve search space) - Learning procedures (remove search altogether!) - Automatically removing bugs in representations

Artform Research Group Some Sample Areas of Machine Learning (there are many, many more..) Machine Learning Similarity-Based Learning Explanation-Based Learning Neural Networks Learning from Examples Learning by Observation Heuristic Induction Symbolic Learning Sub-symbolic learning Genetic Approaches Operator Schema Induction “The Dark Side” Macro Learning Data Mining

Artform Research Group Learning Techniques – “Subsymbolic” Neural Networks n Influenced by brain model n Numerical n Heavily relies on training and examples n Excellent resistance to NOISE Genetic Algorithms (GA’s) n Influenced by ‘evolution’ n Heavily relies on correct ‘fitness’ function n Solutions are heavily ‘coded’ for efficiency ETC..

Artform Research Group Learning Techniques – “Symbolic” … where symbols in a system are used to represent things in the real world Eg n Feature- based generalisation n Explanation-based learning +ve can understand / explain what is going on -ve techniques are not so good for handling noise +ve good for learning complex processes eg planning

Artform Research Group Focus on one area: Data Mining o Data mining is an “off shoot” of Machine Learning that involves discovering patterns from large data bases or data warehouses for different purposes Applications:Data mining and knowledge discovery techniques have been applied to several areas including  Market analysis and Retail  Decision support  Financial analysis  Discovering environmental trends Two Types of Learning: Data Mining can be  “Learning from Example” where we want to learn the features that that charaterise a class eg environmental conditions that lead to an Earthquake  “Learning from Observation” where we have lots of observations and we want the DM to discover interesting patterns

Artform Research Group Data Mining: Inputs Input to Data Mining Algorithms: Sets of “items” – which are like data base records. For example - a “shopping list”, or a might be considered a record where data fields are “nominal” - an environmental observation (temp, wind speed, pressure, wind direction, time) where data fields are more complex – eg real numbers Classification Rule Mining: a class we are interested in characterising (depending on type of learning)

Artform Research Group Data Mining: Outputs Classification Rule Mining: Each item is input with a class C(i) label it is an example of, and OUTPUT is a (set of) classification rules Features => C(1) …. Features => C(n) Association Rule Mining: A set of the most common association rules between features in items are output

Artform Research Group Classification Rule Mining

Artform Research Group Conclusion n Machine Learning is fundamental to AI and involves MANY areas, and many techniques n Data Mining is a particularly fruitful subarea n Next Week we will look at “Association Rule Mining” and “Associative Classification” in particular.