About Machine Learning 2

Machine Learning 

• Introduction
• Concept Learning and the General to Specific Ordering
• Decision Tree Learning
• Artificial Neural Networks
• Bayesian Learning
• Instance-Based Learning
• Genetic Algorithms
• Learning Sets of Rules
• Analytical Learning
• Reinforcement Learning

👉Machine Learning 2 Syllabus

UNIT-1 : Introduction & Concept Learning and the General to Specific Ordering

Introduction- Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning.

Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm.

👉Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes

👉Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs

👉Machine Learning 2- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs

👉Machine Learning 2 - UNIT-1 Questions

UNIT-2: Decision Tree Learning & Artificial Neural Networks

Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.

Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.

👉Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning

👉Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs

👉Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes

👉Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs

👉Machine Learning2 - UNIT 2 : Decision Tree and Artificial Neural Network Questions

UNIT-3: Bayesian Learning & Instance-Based Learning

Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.

Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.

👉Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes

👉Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs

👉Machine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs

👉Machine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs

👉Machine Learning2- UNIT-3 Questions

UNIT-4: Genetic Algorithms & Learning Sets of Rules

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL

👉Machine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs

👉Machine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs

 👉Machine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs

👉Machine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs

👉Machine Learning2: UNIT-4 Questions

UNIT-5: Analytical Learning & Reinforcement Learning

Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.

Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

👉Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

👉Machine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs

👉Machine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs

👉Machine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs

👉Machine Learning2: UNIT-5 Questions

Text Books:

1. Machine Learning – Tom M. Mitchell, – MGH

2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

Reference Books:

1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ. Press.

2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.

 

CONCEPT LEARNING AS SEARCH

 

CONCEPT LEARNING AS SEARCH

·       Concept learning can be viewed as

·       the task of searching through a large space of hypotheses implicitly defined by the hypothesis representation.

·       The goal of this search is to

·       find the hypothesis that best fits the training examples.

Example:

•        Consider the instances X and hypotheses H in the EnjoySport learning task.

•        The attribute

•        Sky has three possible values, and

•        AirTemp, Humidity, Wind, Water, Forecast each have two possible values,

•        the instance space X contains

•        exactly 3*2*2*2*2*2 = 96 distinct instances

•        5*4*4*4*4*4 = 5120 syntactically distinct hypotheses within H.

•        Every hypothesis containing one or more "Φ" symbols represents the empty set of instances; that is, it classifies every instance as negative.

•        1 + (4*3*3*3*3*3) = 973 Semantically distinct hypotheses.

A CONCEPT LEARNING TASK – Instance Space

Hypothesis Space

ü  Similarly there are 5 * 4 * 4 * 4 * 4 * 4 = 5120 syntactically distinct hypotheses within H.

ü  Notice, however, that every hypothesis containing one or more "ø" symbols represents the empty set of instances; that is, it classifies every instance as negative.

ü  Therefore, the number of semantically distinct hypotheses is only 1 + (4 *3 * 3 * 3 * 3 * 3) = 973.

ü  Our EnjoySport example is a very simple learning task, with a relatively small, finite hypothesis space.

General-to-Specific Ordering of Hypotheses

•        To illustrate the general-to-specific ordering, consider the two hypotheses

h1 = (Sunny, ?, ?, Strong, ?, ?)

h2 = (Sunny, ?, ?, ?, ?, ?)

•        Now consider the sets of instances that are classified positive by h1 and by h2. Because h2 imposes fewer constraints on the instance, it classifies more instances as positive.

•        In fact, any instance classified positive by h1 will also be classified positive by h2. Therefore, we say that h2 is more general than h1.

 

More General Than hypothesis

 

·       In the figure,

ü  the box on the left represents the set X of all instances,

ü  the box on the right the set H of all hypotheses.

·       Each hypothesis corresponds to some subset of X

ü  – the subset of instances that it classifies positive.

·       The arrows connecting hypotheses represent

ü   the more - general -than relation,

ü  with the arrow pointing toward the less general hypothesis.

·       Note the subset of instances characterized by

ü     h2 subsumes the subset characterized by h1,

ü     hence  h2 is more - general– than h1.

CONCEPT LEARNING

 

CONCEPT LEARNING

·       Learning involves acquiring general concepts from specific training examples.

·       Example: People continually learn

·       general concepts or

·       categories such as

ü  "bird,"

ü  "car,"

ü  "situations in which I should study more in order to pass the exam," etc.

·       Each such concept can be viewed as

·       describing some subset of objects or events

ü  defined over a larger set.

 

·       Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set.

·       Example: A function defined over all animals, whose value is

·       true for birds and

·       false for other animals.

 

Definition: Concept learning - Inferring a Boolean-valued function from training examples of its input and output

What is Concept Learning…?

•        “A Task of acquiring a potential hypothesis (Solution) that best fits the given training examples”.

5. CONCEPT LEARNING

 

5. CONCEPT LEARNING

·       Learning involves acquiring general concepts from specific training examples. Example: People continually learn general concepts or categories such as "bird," "car," "situations in which I should study more to pass the exam," etc.

·       Each such concept can be viewed as describing some subset of objects or events defined over a larger set

·       Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set. (Example: A function defined over all animals, whose value is true for birds and false for other animals).

Definition: Concept learning - Inferring a Boolean-valued function from training examples of its input and output