Tools of AI. Marcin Sydow. Summary. Machine Learning
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1 Machine Learning
2 Outline of this Lecture Motivation for Data Mining and Machine Learning Idea of Machine Learning Decision Table: Cases and Attributes Supervised and Unsupervised Learning Classication and Regression Examples
3 Data is overwhelming... Each second, our civilisation produces huge amounts of data: WWW server logs (visits to Web pages, clicks) on-line shopping mobile phone connections credit card payments trac sensors (cars, trains, planes) surveillance cameras and sensors stock market transactions scientic measurements (medicine, physics, astrophysics) etc...
4 Motivation for Data Mining Observations: 1 this data is valuable and interesting 2 too much to be analysed and understood by humans 3 data is recorded in electronical form (ideally suited for machine-based analysis and understanding)
5 Main tasks Understanding the data: recognising important patterns in data detecting trends in data (e.g. early detection of economy crisis, etc.) predicting the future based on previous observations classifying new cases grouping cases In machine learning we want the machines do the above, with limited or no support from humans
6 Idea of (Supervised) Learning 1 Learn on previously observed data (supervisor provides correct answers) 2 Apply what you've learnt to new cases
7 Example: a mysterious outdoor game played only in specic weather conditions: outlook temperature humidity windy PLAY? sunny hot high false no sunny hot high true no overcast hot high false yes rainy mild high false yes rainy cool normal false yes rainy cool normal true no overcast cool normal true yes sunny mild high false no sunny cool normal false yes rainy mild normal false yes sunny mild normal true yes overcast mild high true yes overcast hot normal false yes rainy mild high true no
8 Example: Outdoor Game, cont. Basic question: For which weather the game can be played, in general? Even if answer is unknown, there are recorded a couple of cases when the game was or was not played in particular weather conditions. May be, it is possible to infer some knowledge about the good playing conditions so that it could be applied to a new case
9 Outdoor Game - a new case outlook temperature humidity windy PLAY? sunny hot high false no sunny hot high true no overcast hot high false yes rainy mild high false yes rainy cool normal false yes rainy cool normal true no overcast cool normal true yes sunny mild high false no sunny cool normal false yes rainy mild normal false yes sunny mild normal true yes overcast mild high true yes overcast hot normal false yes rainy mild high true no overcast cool high true???
10 Decision Table: Cases and Attributes Knowledge can be built on previously observed cases Each case is described by some attributes of specied type (nominal or numeric) For a given case, each of its attributes has some value (usually) Decision Table: cases = rows attributes = columns (a basic concept in data mining)
11 Outdoor Game Knowledge Representation: Variant 1: Nominal Attributes outlook temperature humidity windy PLAY? sunny hot high false no sunny hot high true no overcast hot high false yes rainy mild high false yes rainy cool normal false yes rainy cool normal true no overcast cool normal true yes sunny mild high false no sunny cool normal false yes rainy mild normal false yes sunny mild normal true yes overcast mild high true yes overcast hot normal false yes rainy mild high true no
12 Outdoor Game Knowledge Representation: Variant 2: Numeric Attributes outlook temperature humidity windy PLAY? sunny false no sunny true no overcast false yes rainy false yes rainy false yes rainy true no overcast true yes sunny false no sunny false yes rainy false yes sunny true yes overcast true yes overcast false yes rainy true no
13 Machine Learning Task: to learn the relationships between the values of attributes in the given knowledge domain (i.e. decision table) Moreover, this knowledge is to be discovered automatically by machine There are two main paradigms: 1 Supervised Learning 2 Unsupervised Learning
14 Supervised Learning 1 Task: predict the correct value of one distinguished attribute ( decision attribute) based on the values of other attributes 2 Learn it on an available set of observed cases ( training set), for which the correct value of the decision attribute is known It is called: classication, when the decision attribute is nominal regression, when the decision attribute is numeric
15 Supervised Learning, summarised input: a new case, with unkown value of its decision attribute (values of other attributes are known) output: the correct value of its decision attribute The system can learn only on some limited number of cases (training set) for which the correct answer is known (provided by a supervisor) In practice: some values in the training set might be distorted, erroneous or even missing training set can be partially inconsistent
16 Classication, example 2 Botany: Species Recognition Consider 3 dierent sub-species of Iris (ower) Iris-setosa Iris-versicolor Iris-virginica Task: learn to identify sub-species of a plant based on sizes of its parts (attributes): sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
17 Species Recognition, cont. Training set: 150 known cases (measured attributes and known correct classication to one of 3 subspecies) The system learns on the training set Subsequently, for any new case, classication is done based on the measurements of its sepals and petals. The knowledge gained in the process of automatic learning is applied to new cases.
18 Iris sub-species recognition Dataset S - iris setosa, V - iris versicolor, VG - iris virginica ll lw pl pw? ll lw pl pw? ll lw pl pw? S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG S V VG
19 Iris classication - Visualisation sepal width / sepal length - not very good separation
20 Iris classication - Visualisation 2 sepal width / petal width - quite good separation
21 How to achieve supervised machine learning? There are many known successful approaches/algorithms: Rule-based Decision trees Bayesian Linear Regression (Feed-forward) Neural Networks (Perceptron, one-layer, multi-layer) Recursive NN Kernel-based approaches (e.g. Support Vector Machines) Instance-based (Nearest Neighbours - knn) other...
22 Examples of Applications of Classication Hand-written digit recognition (classify each written symbol to one of 10 digits) spam detection (classify each to spam or normal) automatic language recognition in text documents (English, Polish, German, etc...) Web document topic classication face recognition oil leakage detection based on satellite images search engine spam detection (spam, normal, borderline)
23 Example: regression Predicting CPU performance based on its parameters Attributes (to base the prediction on): MYCT cycle time (ns) MMIN main memory min MMAX main memory max CACH cache CHMIN channels min CHMAX channels max Decision Attribute (to be predicted): performance (real number)
24 Example: regression MYCT MMIN MMAX CACH CHMIN CHMAX performance
25 Recap Motivation for data mining decision tables, attributes Supervised and unsupervised learning Classication vs regression Approaches to supervised learning (only names) Examples of applications of supervised/unsupervised learning Main tasks of unsupervised learning
26 Questions/Problems: list (other than machine learning) aspects of AI what is the motivation for data mining what is a Decision Table what is Supervised Learning classication vs regression list 4+ approaches to supervised learning
27 Thank you for attention
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