introduction to machine learning fall 2013 comp3710 artificial intelligence computing science...

Introduction to Machine Learning

Fall 2013

Comp3710 Artificial IntelligenceComputing Science

Thompson Rivers University

TRU-COMP3710 Intro to Machine Learning 2

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


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. ...


Chapter Outline1. Introduction2. Training3. Rote Learning4. Learning Concepts5. Inductive Bias6. Decision-Tree Induction7. The Problem of Overfitting8. The Nearest Neighbor Algorithm9. Learning Neural Networks10. Supervised Learning11. Reinforcement Learning12. Unsupervised Learning


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] What are we going to make a machine learn?

Topics


2. Training [Q] How do we learn?

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.


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 ...

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?

Topics


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 valuesSpeed 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, nightTemperature Cold, worm, hot


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”?

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?

Topics

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.


6. Decision Tree Induction

Film Country Big Star Genre Success

Film 1 USA Yes SF True

Film 2 USA No Comedy False

Film 3 USA Yes Comedy True

Film 4 Europe No Comedy True

Film 5 Europe Yes SF False

Film 6 Europe Yes Romance False

Film 7 Other Yes Comedy False

Film 8 Other No SF False

Film 9 Europe Yes Comedy True



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?












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, true and

false. [Q] IF-THEN rules? [Q] Can we use decision tree induction as an expert system?



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.


[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?













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 p1 - ... (if there are other classes)o p0 is the proportion of the training data that are positive (class 0) examples.o p1 is the proportion which are negative (class 1) examples.

[Q] The most certain case?o The entropy of S is 0 when all the examples are positive, or when all the examples

are negative. [Q] The most ambiguous case?

o The entropy reaches its maximum value of 1 when exactly half of the examples are positive, and half are negative.

[Q] The smaller, the better?


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


[Q] The information gain for Country? Gain = 1 – w-entropy(USA) – w-entropy(Europe) – w-entropy(Other) For USA, there are 4 films. 3 out of 4 are True.

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


[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


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





TRU-COMP3710

Intro to Machine Learning22

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.




Topics

[Q] No need to check Genre?


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


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

Topics


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.























Query Thailand Yes Romance ???


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?


[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?

Topics


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.


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 unit, we will discuss them in more detail.

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. Demo: http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html

[Q] Can the positive/negative feedback information be used in training?

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.

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. ...

Topics

introduction to machine learning fall 2013 comp3710 artificial intelligence computing science...

Documents