Introduction to Machine Learning

Introduction to Machine Learning
Fall 2016 Comp3710 Artificial Intelligence Computing Science Thompson Rivers University

Intro to Machine Learning
Course Outline Part I – Introduction to Artificial Intelligence Part II – Classical Artificial Intelligence Part III – Machine Learning Introduction to Machine Learning Neural Networks Probabilistic Reasoning and Bayesian Belief Networks Artificial Life: Learning through Emergent Behavior Part IV – Advanced Topics Genetic Algorithms Fuzzy Reasoning, Fuzzy Expert Systems, Fuzzy Control Systems TRU-COMP3710 Intro to Machine Learning

Learning Objectives Define what classification is. List the three types of attributes with examples. Summarize what concept learning is. Compute the information gain for an attribute from a given training data set. Construct a decision tree from a given training data set, using information gains. ... TRU-COMP3710 Intro to Machine Learning

Chapter Outline Introduction Training Rote Learning Learning Concepts Inductive Bias Decision-Tree Induction The Problem of Overfitting The Nearest Neighbor Algorithm Learning Neural Networks Supervised Learning Reinforcement Learning Unsupervised Learning TRU-COMP3710 Intro to Machine Learning

Topics 1. Introduction [Q] What is learning: Learning and intelligence are intimately related to each other. It is usually agreed that a system capable of learning deserves to be called intelligent. [Q] Is it possible to make a machine learn? We will discuss about the concept of learning methods. [Q] How are we going to make a machine learn? TRU-COMP3710 Intro to Machine Learning

2. Training [Q] How do we learn? How do you know which one is apple? Learning problems usually involve classifying inputs into a set of classifications. Example of the people in this class [Q] Can we classify us? Learning is only possible if there is a relationship between the data and the classifications. [Q] What kind of relationship? How to classify? Based on similarity Training involves providing the system with data which has been manually classified. Learning systems use the training data to learn to classify unseen data. TRU-COMP3710 Intro to Machine Learning

Topics 3 data types based on comparison methods The input data usually consist of multiple attributes. The attributes are not all of the same type. Numeric or numerical: E.g., 0, 1, .67, ... ; order and distance between two values Ordinal: E.g., Small, Medium, Large, Extra Large; only order Categorical (Boolean can be considered this type.): English, Chines, Spanish; not even order Example of the people in this class Age numerical, ordinal, or categorical? Height ? Weight ? Mother tongue ? Hair color ? Number of legs ? Number of arms Is it really necessary to classify us? Number of nose ... TRU-COMP3710 Intro to Machine Learning

Topics 3. Rote Learning Simply involves memorizing the classifications of the training data. A very simple learning method. Can only classify previously seen data – unseen data cannot be classified by a rote learner. [Q] Is this learning type good enough? What if we have a similar data? TRU-COMP3710 Intro to Machine Learning

4. Concept Learning Concept learning involves determining a mapping from a set of input variables to a Boolean value. Such methods are known as inductive learning methods. If a function can be found which maps training data to correct classifications, then it will also work well for unseen data – hopefully! This process is known as generalization. A simple toy problem to determine whether driving in a particular manner in particular road conditions is safe or not: Attribute Possible values Speed Slow, medium, fast Type? Weather Wind, rain, snow, sun Type? Distance from car in front 10ft, 20ft, 20ft, ... Type? Units of alcohol driver has drunk 0, 1, 2, 3, 4, 5 Type? Time of day Morning, afternoon, evening, night Temperature Cold, worm, hot TRU-COMP3710 Intro to Machine Learning

Topics A hypothesis (or object) is a vector (or list) of attributes: h1 = <slow, wind, 30ft, 0, evening, cold> h2 = <fast, rain, 10ft, 2, ?, ?> ? means “we do not care”, i.e., any value This looks clearly untrue. -> Negative training example h3 = <fast, rain, 10ft, 2, , >  means no value In concept learning, a training hypothesis is either a positive or negative (true or false) (or multiple classes). Concept learning can be thought as search through a search space that consists of hypotheses, where the goal is the hypothesis that is most closely mapped to a given query. [Q] How to define “closely”? TRU-COMP3710 Intro to Machine Learning

Topics 5. Inductive Bias All learning methods have an inductive bias. Inductive bias refers to the restrictions that are imposed by the assumptions made in the learning method. E.g., the solution to the problem of road safety can be expressed as a conjunction of a set of six concepts. This does not allow for more complex expressions that cannot be expressed as a conjunction. Therefore there can be some potential solutions that we cannot explore. However, without inductive bias, a learning method could not learn to generalize. Occam’s razor is an example of an inductive bias: The best hypothesis to select is the simplest one. h1 = <slow, wind, 30ft, 0, evening, cold> h2 = <slow, wind, 30ft, ?, ?, ?> Which one is better? TRU-COMP3710 Intro to Machine Learning

6. Decision Tree Induction
Box-office success problem Training data set: [Q] How to obtain this kind of table? Query: (Thailand, Yes, Romance) -> Success? [Q] How many comparisons when brute-force search is used? Let’s try. Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 TRU-COMP3710 Intro to Machine Learning

In the box-office success problem, what must be the first question? Country, Big Start, or Genre? Country is a significant determinant of whether a film will be a success or not. Hence the first question is Country. What is the next then? Using a tree? [Q] How to determine which factor is the most significant determinant? Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 TRU-COMP3710 Intro to Machine Learning

A decision tree takes an input and gives a Boolean output (or a class). Box-office success problem: Decision trees can represent more complex expressions, involving disjunctions and conjunctions. ((Country = USA)  (Big Star = Yes))  ((Country = Europe)  (Genre = Comedy)) All the objects in the training data set have classes. In this example, the classes are ‘true’ and ‘false.’ [Q] IF-THEN rules? [Q] Can we use decision tree induction as an expert system? TRU-COMP3710 Intro to Machine Learning

Decision tree induction involves creating a decision tree from a set of training data that can be used to correctly classify the training data. A query will be given without class to find the class to which the query fits most. [Q] How to create an efficient decision tree from a training data set? ID3, developed in the 1980s, is an example of a decision tree learning algorithm. ID3 builds the decision tree from the top down, selecting the attributes (or also called features) from the training data that provide the most information at each stage. That is, most determinant attribute. TRU-COMP3710 Intro to Machine Learning

[Q] In the box-office success problem, what must be the first question? Country, Big Start, or Genre? Country is a significant determinant of whether a film will be a success or not. Hence the first question is Country. [Q] How do we know what the most significant determinant is? Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 TRU-COMP3710 Intro to Machine Learning

ID3 selects attributes based on information gain. Information gain is the reduction in entropy caused by a decision. In information theory, entropy is a measure of the uncertainty associated with a random variable. Entropy of a set is defined as: H(S) = - p0 log2 p0 - p1 log2 p (if there are other classes) p0 is the proportion of the training data that are positive (class 0) examples. p1 is the proportion which are negative (class 1) examples. [Q] The most certain case? The entropy of S is 0 when all the examples are positive, or when all the examples are negative. How? [Q] The most ambiguous case? The entropy reaches its maximum value of 1 when exactly half of the examples are positive, and half are negative. How? [Q] The smaller, the better? TRU-COMP3710 Intro to Machine Learning

The information gain of a particular attribute tells us how closely the attribute represents the entire target function, and so at each stage, the attribute that gives the highest information gain is chosen to turn into a question. [Q] How to obtain the information gain of a particular attribute? Gain = 1 –  (the weighted entropy for each value in the attribute) Then, 0 <= Gain <= 1 Gain shows the certainty of an attribute. TRU-COMP3710 Intro to Machine Learning

Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 [Q] The information gain for Country? Gain = 1 – w-entropy(USA) – w-entropy(Europe) – w-entropy(Other) = 1 – 4/10 * H(USA) – 4/10 * H(Europe) – 2/10 * H(Other) For USA, there are 4 films. 3 out of 4 are True, and 1/4 is False. H(USA) = – 3/4 log2 (3/4) – (1/4) log2 (1/4) = .811 Similarly, (2/4 are True, and 2/4 are False for Europe; 2/2 are False for Other) H(Europe) = 1; H(Other) = 0 The weights for USA, Europe and Other are 4/10, 4/10 and 2/10. Gain = 1 – (4/10 * .811) – (4/10 * 1) – (2/10 * 0) = .2756 H(S) = - p0 log2 p0 - p1 log2 p1 TRU-COMP3710 Intro to Machine Learning

Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 [Q] The information gain for Big Star? For Yes, there are 7 films. 4 out of 7 are True. H(Yes) = –4/7 log2 (4/7) – (3/7) log2 (3/7) = ? Similarly, H(No) = ? The weights for Yes and No are 7/10 and 3/10. Gain = 1 – (7/10 * ?) – (3/10 * ?) = .01 Gain for Genre = .17 H(S) = - p0 log2 p0 - p1 log2 p1 TRU-COMP3710 Intro to Machine Learning

The information gain for Country= .2756 The information gain for Big Star = .01 The information gain for Genre = .17 Therefore, the attribute Country provides the greatest information gain and so is placed at the top of the decision tree. This method is then applied recursively to the sub-branches of the tree. For example of USA, we need to decide the most determinant among BigStar and Genre. Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 10 TRU-COMP3710 Intro to Machine Learning

Film Country Big Star Genre Success Film 7 Other Yes Comedy False Film 8 No SF The case of Country = Other after Country is selected: The information gain for Big Star: For Yes, there is 1 film. 1 out 1 is False. -> H(Yes) = 0 For No, there is 1 film. 1 out 1 is False. -> H(No) = 0 Gain = 1 – 1/2 * 0 – 1/2 * 0 = 1 Similarly, the information gain for Genre = 1 Hence there is no more branch in this case. [Q] The meaning is ??? This method is then applied recursively to other sub-branches of the tree. H(S) = - p0 log2 p0 - p1 log2 p1 [Q] No need to check Genre? TRU-COMP3710 Intro to Machine Learning

Topics You may read this link – You can find a similar example from the above link. What if attributes are not categorical? What if they are ordinal or numerical? Hint: Two classes – one class <= a value; another class > the value Or find meaningful groups. It is called clustering. Fuzzy decision trees? TRU-COMP3710 Intro to Machine Learning

7. The Problem of Overfitting
Noise can be in the training data set. The training data do not adequately represent the entire space of possible data. Then, for example, a decision tree can perform poorly at classifying unseen data. Not only decision trees, but also other learning methods. TRU-COMP3710 Intro to Machine Learning

Topics Black dots represent positive examples, white dots negative. The two lines represent two different hypotheses. In the first diagram, there are just a few items of training data, correctly classified by the hypothesis represented by the darker line. In the second and third diagrams we see the complete set of data, and that the simpler hypothesis which matched the training data less well matches the rest of the data better than the more complex hypothesis, which overfits. noise TRU-COMP3710 Intro to Machine Learning

8. The Nearest Neighbor Algorithm
[Q] Any other learning method? The Nearest Neighbor algorithm is an example of instance based learning. Instance based learning involves storing training data and using it to attempt to classify new data as it arrives. Film Country Big Star Genre Success Film 1 USA Yes SF True Film 2 No Comedy False Film 3 Film 4 Europe Film 5 Film 6 Romance Film 7 Other Film 8 Film 9 Film 10 Film Country Big Star Genre Success Film 1 USA Yes SF True Film 3 Comedy Film 4 Europe No Film 9 Film 2 False Film 5 Film 6 Romance Film 7 Other Film 8 Query Thailand ??? TRU-COMP3710 Intro to Machine Learning

The nearest neighbor algorithm works with data that consists of vectors of numerical attributes. Each vector represents a point in n-dimensional space. When an unseen data item is to be classified, the Euclidean distance is calculated between this item and all training data. For example, the distance between <x1, y1> and <x2, y2> is: [Q] How to classify? TRU-COMP3710 Intro to Machine Learning

Topics [Q] How to classify, when k = 3? The k-nearest neighbor algorithm: The classification for the unseen data is usually selected as the one that is most common amongst the k-nearest neighbors. Shepard’s method: This involves allowing all training data to contribute to the classification with their contribution being proportional to their distance from the data item to be classified. For each class, 1/di ... Advantage: Unlike decision tree learning, the nearest neighbor algorithm performs very well with noisy input data. Disadvantage: [Q] But, what if the training data set is huge? Any good idea? TRU-COMP3710 Intro to Machine Learning

9. Neural Networks An artificial neural network is a network of artificial neurons, which is based on the operation of the human brain. Neural networks usually have their nodes arranged in layers. One layer is the input layer, and another is an output layer. There are one or more hidden layers between these two. TRU-COMP3710 Intro to Machine Learning

Topics The connections between nodes have weights associated with them, which determine the behavior of the network. Input data is applied to the input layer. Neurons fire if their inputs are above a certain level. If one neuron is connected to another the firing of one may cause the firing of the next. In the next chapter, we will discuss them in more detail. TRU-COMP3710 Intro to Machine Learning

Topics 10. Supervised Learning Two types of learning Supervised, Unsupervised Supervised learning learns by being presented with pre-classified training data set. [Q] Is decision tree induction supervised learning? [Q] Is the k-nearest neighbor algorithm supervised learning? Many neural networks use supervised learning. Pre-classified training data is provided to the network before it is presented with unseen data. The training data causes the weights in the network to be set to levels such that unseen data can be classified correctly. Neural networks are able to learn to classify extremely complex functions. [Q] Can the positive/negative feedback information be used in training? TRU-COMP3710 Intro to Machine Learning

11. Reinforcement Learning
Topics 11. Reinforcement Learning Systems that learn using reinforcement learning are given a positive feedback when they classify data correctly, and negative feedback when they classify data incorrectly. Credit assignment is needed to reward nodes in a network correctly. TRU-COMP3710 Intro to Machine Learning

12. Unsupervised Learning
Topics 12. Unsupervised Learning Unsupervised learning learns without any training data set. Unsupervised learning networks learn without requiring human intervention. No training data is required. [Q] How is that possible? The system learns to cluster input data into a set of classifications that are not previously defined. This is called clustering. Clustering is a basic tool for data mining and pattern recognition. Example: Fuzzy C-Means, EM, Kohonen Maps. ... TRU-COMP3710 Intro to Machine Learning

Introduction to Machine Learning

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