COM3250 / COM6170 Introduction to Machine Learning

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1 COM3250 / COM6170 Introduction to Machine Learning Instructor Prof. Rob Gaizauskas r.gaizauskas@dcs.shef.ac.uk Office: Room Computer Science Classes Lecture 1: Monday 11:10 am, SG LT05 (LT05, St. George s Complex) Lecture 2: Tuesday 10:00 am, SG LT18 (LT18, St. George s Complex) Tutorial/Lab: Tuesday 11:10 pm, SG LT18 (LT18, St. George s Complex) Homepage robertg/campus_only/com3250/ Assessment UG & MSc: Coursework: 30% 2 assignments (10% + 20%) Examination: 70 % RTP: See instructor Reading Weeks Office Hours To be announced requests for appointment COM3250 /

2 Course Aims and Objectives Aims to introduce some of the main topics in machine learning, especially automated concept learning or classification from examples, but also learning association rules and clustering to develop skills in designing and building serious artificial intelligence programs Objectives By the end of this course the students should: understand some of the main approaches/algorithms that are used for representing concepts and learning them automatically; be able to analyse datasets and transform them into representations that will permit machine learning algorithms learn concepts/discover patterns in them be able to evaluate competing machine learning algorithms over the same datasets COM3250 /

3 Course Presuppositions knowledge of basic artificial intelligence techniques, e.g. from COM1080 Advisable: some knowledge of machine reasoning techniques, e.g from COM3290 COM3250 /

4 Course Structure (Proposed) Introduction to Machine Learning (Lecture 1) Concept Learning (Lecture 2 and 3) Decision Tree Learning (Lecture 4 and 5) Evaluating Hypotheses (Lecture 6 and 7) Bayesian Learning, Bayesian Classifiers, Bayesian Belief Networks (Lecture 8 10) Instance-based Learning (Lecture 11) Rule Set Learning, Induction and Inductive Logic Programming (Lecture 12 14) Computational Learning Theory (Lecture 15 and 16) Linear Models and Neural Networks (Lecture 17 and 18) Association Rule Mining and Clustering (Lecture 19) Combining Multiple Models (Lecture 20) COM3250 /

5 References Primary Texts T. Mitchell. Machine Learning. WCB/McGraw-Hill, Boston, tom/mlbook.html I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edition, Morgan Kaufmann, San Francisco, Also useful S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (2nd ed), Pearson Education, N. J. Nilsson. Introduction to Machine Learning, Draft of a proposed textbook, Available at: COM3250 /

6 Introduction to Machine Learning Lecture Outline: What is Machine Learning? Why Study Machine Learning? Applications of Machine Learning Varieties of Learning WEKA: Software for Machine Learning Reading: Chapter 1 of Mitchell Chapter 1 of Witten & Frank COM3250 /

7 What is Machine Learning? A possible definition: The study of how to design computer programs whose performance at some task improves through experience. or more precisely (Mitchell): Definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T as measured by P improves with experience E Important issue: Are we only interested in performance of learning program? Or are we also interested in discovering human-comprehensible descriptions of patterns in data? (knowledge discovery) COM3250 /

8 What is Machine Learning? (cont) Disciplines contributing to Machine Learning Statistics Artificial Intelligence Philosophy Information Theory Biology Cognitive Science Computational Complexity Control Theory COM3250 /

9 Why Study Machine Learning? technological or engineering motivation to build computer systems that can improve their performance at tasks with experience (data) massive growth in on-line data computer science motivation to understand better properties of various algorithms for function approximation how the data must be represented how much data they require how accurate they can be how to choose optimal data for training cognitive science motivation to understand better how humans learn by modelling the learning process COM3250 /

10 (Some) Areas of Application of Machine Learning Data mining: using historical data to improve decisions increasingly important given explosion of electronic data. E.g.: Medicine: medical records medical knowledge selecting best embyros from in vitro fertilisation based on 60 features of embryos and historical data on viability Business: customer records better business decisions assessing credit-worthiness of loan applicants based on features of former borrowers and repayment outcomes improving customer retention based on discovering patterns of features amongst loyal vs. defecting customers Agriculture herd/crop records better farming decisions improving cull selection from dairy herds by data mining over database of 700 attributes of millions of cows... COM3250 /

11 (some) Areas of Application of Machine Learning (cont) Software applications we can t program by hand autonomous driving speech recognition sonar signal classification fingerprint classification Personalised/self-customising programs Newsreader that learns user interests Recommender/advertisting systems tailored to learned user profile Computer Games Heuristic evaluation functions for combinatorily explosive board games (chess, checkers, Othello,... ) COM3250 /

12 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence COM3250 /

13 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence face recognition input a test image and an image(s) of a known face and output a yes/no if test image contains a face matching the known face COM3250 / a

14 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence face recognition input a test image and an image(s) of a known face and output a yes/no if test image contains a face matching the known face load forecasting in the electricity industry output estimates of maximum and minimum load for each hour, day, month, season given historical data about electricity, weather COM3250 / b

15 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence face recognition input a test image and an image(s) of a known face and output a yes/no if test image contains a face matching the known face load forecasting in the electricity industry output estimates of maximum and minimum load for each hour, day, month, season given historical data about electricity, weather credit assessment input set of features describing applicant (gender, age, postcode, etc.) and output yes/no or probability of defaulting, indicating credit worthiness COM3250 / c

16 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence face recognition input a test image and an image(s) of a known face and output a yes/no if test image contains a face matching the known face load forecasting in the electricity industry output estimates of maximum and minimum load for each hour, day, month, season given historical data about electricity, weather credit assessment input set of features describing applicant (gender, age, postcode, etc.) and output yes/no or probability of defaulting, indicating credit worthiness games input set of features describing board position (e.g. chess) output the next move COM3250 / d

17 Varieties of Learning What is to be learned? a computational structure of some sort. Could be (Nilsson, 1997): function program finite state machine grammar problem solving system Much ML set in the context of learning input-output functions. Includes applications such as character and speech recognition input a graphical representation of character sequence or a waveform and output a character or word sequence face recognition input a test image and an image(s) of a known face and output a yes/no if test image contains a face matching the known face load forecasting in the electricity industry output estimates of maximum and minimum load for each hour, day, month, season given historical data about electricity, weather credit assessment input set of features describing applicant (gender, age, postcode, etc.) and output yes/no or probability of defaulting, indicating credit worthiness games input set of features describing board position (e.g. chess) output the next move Useful to begin by introducing basic concepts in the setting of learning functions... COM3250 / e

18 Function Learning as Curve-fitting (Wavemetrics.com) (Mathworks.com) COM3250 /

19 Learning Input-Output Functions: General Setting Typically we are trying to learn a function f. f is often referred to as the target function f takes a vector-valued input an n-tuple x = (x 1,x 2,...x n ) f itself may be vector-valued, yielding a k-tuple as output. Often f produces a single output value, i.e. k = 1 (in such cases the output is not thought of as a vector) COM3250 /

20 Learning Input-Output Functions: General Setting Typically we are trying to learn a function f. f is often referred to as the target function f takes a vector-valued input an n-tuple x = (x 1,x 2,...x n ) f itself may be vector-valued, yielding a k-tuple as output. Often f produces a single output value, i.e. k = 1 (in such cases the output is not thought of as a vector) Job of the learner is to output a hypothesis h which is its guess or approximation of the target function f h is assumed a priori to be drawn from a class of functions H Note that f may or may not be in H and this may/may not be known COM3250 / a

21 Learning Input-Output Functions: General Setting Typically we are trying to learn a function f. f is often referred to as the target function f takes a vector-valued input an n-tuple x = (x 1,x 2,...x n ) f itself may be vector-valued, yielding a k-tuple as output. Often f produces a single output value, i.e. k = 1 (in such cases the output is not thought of as a vector) Job of the learner is to output a hypothesis h which is its guess or approximation of the target function f h is assumed a priori to be drawn from a class of functions H Note that f may or may not be in H and this may/may not be known The learner selects h based on a set Ξ of training examples. Each training example consists of an n-tuple x drawn from the set of input vectors over which f is defined if for each training example x the value of f, i.e. f(x), is also supplied then the learning setting is called a supervised learning setting most of the methods we investigate are supervised learning methods if the training examples do not have associated output values, then the learning setting is called unsupervised learning useful for splitting datasets into partitions, aka clustering COM3250 / b

22 Learning Input-Output Functions: General Setting (cont) x = x 1 x 2. Target function f y or y 1 y 2. x n y k Training Examples Ξ = {x 1,...x m } + f(x i ) for each x i Ξ Hypothesis Space H Learner h, where h f h 1 h 2 h i h k COM3250 /

23 Learning Input-Output Functions: Input Vectors Input vectors go by a variety of names in the ML literature: input vector, pattern vector, feature vector, sample, example, instance COM3250 /

24 Learning Input-Output Functions: Input Vectors Input vectors go by a variety of names in the ML literature: input vector, pattern vector, feature vector, sample, example, instance Components x i of input vectors variously called: features, attributes, input variables, components. COM3250 / a

25 Learning Input-Output Functions: Input Vectors Input vectors go by a variety of names in the ML literature: input vector, pattern vector, feature vector, sample, example, instance Components x i of input vectors variously called: features, attributes, input variables, components. Values of components generally of two major sorts: numeric also called ordinal and dividing into real-valued and discrete-valued numbers nominal also called categorical, enumerated or (confusingly) discrete COM3250 / b

26 Learning Input-Output Functions: Input Vectors Input vectors go by a variety of names in the ML literature: input vector, pattern vector, feature vector, sample, example, instance Components x i of input vectors variously called: features, attributes, input variables, components. Values of components generally of two major sorts: numeric also called ordinal and dividing into real-valued and discrete-valued numbers nominal also called categorical, enumerated or (confusingly) discrete Example: A loan applicant Fred might be an instance to a learner represented by the 6-tuple: (m,27,1,3,u,24376) where the attributes are gender, age, in-work, years-at-current-job, education-code, salary gender is nominal (values m or f) age is ordinal (discrete-valued number) in-work is nominal a special case of a boolean-valued attribute years-at-current-job is ordinal (discrete-valued number) education-code is nominal (some fixed set of code values, e.g. u = undergraduate degree) salary is ordinal (discrete-valued number) COM3250 / c

27 Learning Input-Output Functions: Outputs If a learner produces a hypothesis h that outputs a real-number it is called function estimator its output is called an output value or estimate If a learner produces a hypothesis h that outputs a categorical value it is called classifier, recognizer or categorizer its output is called a label, class, category or decision Outputs may also be vector-valued with the components being numeric or nominal (or both) Example: In the previous example assessing loans applicants the learned hypothesis could be a function estimator if the output is a real-number approximating the probability of Fred defaulting a classifier if the output is a boolean 1 or 0 indicating whether Fred should be given a loan or not COM3250 /

28 Learning Input-Output Functions: Training Regimes Considerable variation possible in how training examples are presented/used. COM3250 /

29 Learning Input-Output Functions: Training Regimes Considerable variation possible in how training examples are presented/used. In the batch method all training examples are available and used all at once to compute h in a variant of this all training examples are used iteratively to refine a current hypothesis until some stopping condition COM3250 / a

30 Learning Input-Output Functions: Training Regimes Considerable variation possible in how training examples are presented/used. In the batch method all training examples are available and used all at once to compute h in a variant of this all training examples are used iteratively to refine a current hypothesis until some stopping condition In the incremental method one member of the training set is selected at a time and used to modify the current hypothesis members of the training set can be selected at random (with replacement) or the set can be cycled through iteratively if training instances become available one at a time and are used as they become available. the method is called an on-line method e.g. a robot which is learning a hypothesis from sensory inputs which controls its actions (and hence determines its future sensory inputs) COM3250 / b

31 Learning Input-Output Functions: Training Regimes Considerable variation possible in how training examples are presented/used. In the batch method all training examples are available and used all at once to compute h in a variant of this all training examples are used iteratively to refine a current hypothesis until some stopping condition In the incremental method one member of the training set is selected at a time and used to modify the current hypothesis members of the training set can be selected at random (with replacement) or the set can be cycled through iteratively if training instances become available one at a time and are used as they become available. the method is called an on-line method e.g. a robot which is learning a hypothesis from sensory inputs which controls its actions (and hence determines its future sensory inputs) Training may be direct each training instance has associated target value indirect only sequences of training instances have associated outcome e.g. in chess want to learn function from board position to next move may not know whether individual moves are correct only whether sequence of moves leads to win or loss learner must decide what moves in sequence are good/bad (the credit assignment problem) COM3250 / c

32 Learning Input-Output Functions: Performance Evaluation Important to know how well our learner is doing, i.e. how good the hypothesis it produces is Standard approach is to test hypothesis on a set of inputs and function values not used in training, referred to as the test set Common evaluation measures used are mean-squared error and accuracy (= proportion of instances misclassified) COM3250 /

33 Summary Machine learning is about creating programs which improve their performance on a given task with experience Aside from performance alone, we may also be interested in discovering patterns in our data which we can understand ML is increasingly important in applications in all walks of life given the exponential increase in electronic data ML also of interest to theoretical computer scientists and to cognitive scientists interested in modelling human learning COM3250 /

34 Summary (cont) The general ML setting involves: identifying target function to be learned its inputs and outputs and how they are to be represented identifying and collecting the training set determining the training regime (batch, incremental, on-line, direct/indirect) choosing a learning algorithm which approximates the target function evaluating the learned hypothesis against some held-out test data COM3250 /

35 WEKA: Software for Machine Learning For labs and assignments we will be using Weka a software platform for experimenting with and learning about machine learning algorithms (complements Witten & Frank book) Weka 3 is available from the University of Waikato in New Zealand at: If you have your own laptop you are advised to download and install a copy. A recent version of Weka-3.* should already be available on the DCS Linux Desktop, under the directory /usr/local/pkg/weka-3.* on the DCS XP Desktop under Start>All Programs>Weka Have a play! (see sample data files in data directory that comes with it) COM3250 /

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