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machine learning

machine learning. learning... a fundamental aspect of intelligent systems – not just a short-cut to kn acquisition / complex behaviour. learning. not well understood by philosophy, psychology, physiology AI asks: how can a machine... generate rules from case histories

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machine learning

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  1. machine learning learning... a fundamental aspect of intelligent systems – not just a short-cut to kn acquisition / complex behaviour

  2. learning • not well understood by philosophy, psychology, physiology • AI asks: how can a machine... • generate rules from case histories • reorganise its kn as it expands • generate its own s/w • learn to discriminate diff. phenomenon

  3. learning • not well understood by philosophy, psychology, physiology • AI asks: how can a machine... • generate rules – induction • reorganise kn – generalisation & induction • generate s/w – evolution (& others?) • discriminate phenomena – neural nets (etc)

  4. implementation issues • supervised vs unsupervised • with / without known outputs • classification vs regression • discrete versus continuous values • stuff learned “inductive bias” • types of functions learned • algorithms restrict what is learned

  5. a generic model(?)

  6. lecture programme • induction intro • rules from semantic nets (tutorial) • nearest neighbour • splitting feature space • forming decision trees (& then rules) • generalisation (semantic nets) • near-miss • evolution • neural networks

  7. induction #1 • def: “automatically acquire a function from inputs to output values, based on previously seen inputs and output values” • input: feature values • output: classification • eg: speech recognition, object identification

  8. induction #2 • aims: generate rules from examples • formally: given collection of {x → y} find hypothesis h : h(x) ≈ y for (nearly) all x & y

  9. issues • feature selection • what features to use? • how do they relate to each other? • how sensitive is the technique to feature selection? (irrelevant, noisy, absent feature; feature types) • complexity & generalisation • matching training data vs performance on new data NB: some of following slides are based on examples from Machine learning programme at The University of Chicago Department of Computer Science

  10. induction – principles • Occam’s razor the world is inherently simple so the most likely hypothesis is the simplest one which is consistent with observations • other • use -ve as well as +ve evidence • seek concomitant variation in cause/result • more frequently observed associations are more reliable

  11. rules from semantic nets • a tutorial problem • think bicycle, cart, car, motor-bike • build nets from examples (later) • build rules from nets

  12. nearest neighbour • supervised, classification (usually) • training: record inputs  outputs • use: • find “nearest” trained case & return associated output value • +ve: fast, general purpose • -ve: expensive prediction, definition of distance is complex, sensitive to noise

  13. feature space splitting • supervised, classification • training: record inputs  outputs • +ve: fast, tolerant to (some) noise • -ve: some limitations, issues about feature selection, etc

  14. splitting feature-space

  15. splitting feature-space

  16. splitting feature-space

  17. splitting feature-space

  18. splitting feature-space • real examples have many dimensions • splitting by clusters can give “better” rules • wider empty zones between clusters give “better” rules

  19. identification trees • (aka “decision trees”) • supervised, classification • +ve: copes better with irrelevant attributes & noise, fast in use • -ve: more limited that nearest neighbour (& feature space)

  20. ID trees • train: build tree by forming subsets of least disorder • use: • traverse tree based on feature tests & assign leaf node label • OR: use a ruleset • +ve: robust to irrelevant features & some noise, fast prediction, readable rules • -ve: poor feature combination, poor handling of feature dependencies, optimal trees not guaranteed

  21. identification trees

  22. sunburn • goal: predict burn/no burn for new cases • cannot do exact match (same features) same output (feature space too large) • nearest neighbour?but: what is close? which features matter?

  23. Sunburn Identification Tree Hair Color Blonde Brown Red Lotion Used Emily: Burn Alex: None John: None Pete: None No Yes Sarah: Burn Annie: Burn Katie: None Dana: None

  24. building ID trees • aim: build a small tree such that all samples at leaves have same label • at each node, pick tests so branches are closest to having same class • Split into subsets with least “disorder” • (Disorder ~ Entropy) • find test that minimizes disorder

  25. Minimizing Disorder Hair Color Height Brown Blonde Tall Short Red Average Alex:N Annie:B Katie:N Sarah:B Emily:B John:N Sarah: B Dana: N Annie: B Katie: N Alex: N Pete: N John: N Dana:N Pete:N Emily: B Lotion Weight Yes No Heavy Light Average Sarah:B Annie:B Emily:B Pete:N John:N Dana:N Alex:N Katie:N Dana:N Alex:N Annie:B Emily:B Pete:N John:N Sarah:B Katie:N

  26. Minimizing Disorder Height Tall Short Average Annie:B Katie:N Sarah:B Dana:N Lotion Weight Yes No Heavy Light Average Sarah:B Annie:B Dana:N Katie:N Dana:N Annie:B Sarah:B Katie:N

  27. measuring disorder • Problem: • large DB’s don’t yield homogeneous subsets • Solution: • IS defines theoretic measure of disorder • Homogeneous set: least disorder = 0 • Even split: most disorder = 1

  28. sunburn entropy #1 Hair color = 4/8(-2/4 log 2/4 - 2/4log2/4) + 1/8*0 + 3/8 *0 = 0.5 Height = 0.69 Weight = 0.94 Lotion = 0.61

  29. sunburn entropy #2 Height = 2/4(-1/2log1/2-1/2log1/2) + 1/4*0+1/4*0 = 0.5 Weight = 2/4(-1/2log1/2-1/2log1/2) +2/4(-1/2log1/2-1/2log1/2) = 1 Lotion = 0

  30. building ID trees with disorder • until each leaf is as homogeneous as possible • select a non-homogeneous leaf node • replace node by a test creating subsets with least average disorder

  31. features in ID Trees: Pros • Feature selection: • Tests features that yield low disorder • E.g. selects features that are important! • Ignores irrelevant features • Feature type handling: • Discrete type: 1 branch per value • Continuous type: Branch on >= value • Need to search to find best breakpoint • Absent features: Distribute uniformly

  32. Features in ID Trees: Cons • features assumed independent • If want group effect, must model explicitly • E.g. make new feature AorB • feature tests conjunctive

  33. From ID Trees to Rules Hair Color Blonde Brown Red Lotion Used Emily: Burn Alex: None John: None Pete: None No Yes Sarah: Burn Annie: Burn Katie: None Dana: None (if (equal haircolor blonde) (equal lotionused yes) (then None)) (if (equal haircolor blonde) (equal lotionused no) (then Burn)) (if (equal haircolor red) (then Burn)) (if (equal haircolor brown) (then None))

  34. generalisation has yellow

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