CS8082 Machine Learning Techniques Syllabus Regulation 2017
UNIT I INTRODUCTION
Learning Problems – Perspectives and Issues – Concept Learning –
Version Spaces and Candidate Eliminations – Inductive bias – Decision
Tree learning – Representation – Algorithm – Heuristic Space Search.
UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS
Neural Network Representation – Problems – Perceptrons – Multilayer
Networks and Back Propagation Algorithms – Advanced Topics –
Genetic Algorithms – Hypothesis Space Search – Genetic Programming
– Models of Evaluation and Learning.
UNIT III BAYESIAN AND COMPUTATIONAL LEARNING
Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum
Description Length Principle – Bayes Optimal Classifier – Gibbs
Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM
Algorithm – Probability Learning – Sample Complexity – Finite and
Infinite Hypothesis Spaces – Mistake Bound Model.
UNIT IV INSTANT BASED LEARNING
K- Nearest Neighbour Learning – Locally weighted Regression – Radial
Bases Functions – Case Based Learning.
UNIT V ADVANCED LEARNING
Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule
Set – First Order Rules – Sets of First Order Rules – Induction on Inverted
Deduction – Inverting Resolution – Analytical Learning – Perfect Domain
Theories – Explanation Base Learning – FOCL Algorithm –
Reinforcement Learning – Task – Q-Learning – Temporal Difference
Learning
,UNIT I INTRODUCTION
Definition of Machine Learning (Mitchell 1997) — “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 the tasks
improves with the experiences”
Spam Mail detection learning problem
1. Task T: To recognize and classify emails into ‘spam’ or ‘not spam’.
2. Performance measure P: Total percent of mails being correctly
classified as ‘spam’ (or ‘not spam’ ) by the program.
3. Training experience E: A set of mails with given labels (‘spam’ /
‘not spam’).
Simple learning process
For any learning system, we must be knowing the three elements — T
(Task), P (Performance Measure), and E (Training Experience). At a
high level, the process of learning system looks as below.
,The goal of the learning process is to find the final hypothesis that best
approximates the unknown target function.
Design of a learning system
1. Type of training experience
2. The exact type of knowledge to be learned (Choosing the Target
Function). Initially, the target function will be unknown.
3. A representation for this target knowledge (Choosing a representation
for the Target Function)
4. A learning mechanism (Choosing an approximation algorithm for the
Target Function)
, 1. Task T: To play checkers
2. Performance measure P: Total percent of the game won in the
tournament.
3. Training experience E: A set of games played against itself
Training experience
1. Direct or Indirect training experience
Direct : An individual board states and correct move for each board
state are given.
Indirect: The move sequences for a game and the final result (win,
loss or draw) are given for a number of games.
2. Supervised / Unsupervised:
Supervised: board states will be labeled with the correct move.
Unsupervised: The training experience will be unlabeled, which
means, all the board states will not have the moves. So the learner
generates random games and plays against itself with no
supervision.
3. Performance
UNIT I INTRODUCTION
Learning Problems – Perspectives and Issues – Concept Learning –
Version Spaces and Candidate Eliminations – Inductive bias – Decision
Tree learning – Representation – Algorithm – Heuristic Space Search.
UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS
Neural Network Representation – Problems – Perceptrons – Multilayer
Networks and Back Propagation Algorithms – Advanced Topics –
Genetic Algorithms – Hypothesis Space Search – Genetic Programming
– Models of Evaluation and Learning.
UNIT III BAYESIAN AND COMPUTATIONAL LEARNING
Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum
Description Length Principle – Bayes Optimal Classifier – Gibbs
Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM
Algorithm – Probability Learning – Sample Complexity – Finite and
Infinite Hypothesis Spaces – Mistake Bound Model.
UNIT IV INSTANT BASED LEARNING
K- Nearest Neighbour Learning – Locally weighted Regression – Radial
Bases Functions – Case Based Learning.
UNIT V ADVANCED LEARNING
Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule
Set – First Order Rules – Sets of First Order Rules – Induction on Inverted
Deduction – Inverting Resolution – Analytical Learning – Perfect Domain
Theories – Explanation Base Learning – FOCL Algorithm –
Reinforcement Learning – Task – Q-Learning – Temporal Difference
Learning
,UNIT I INTRODUCTION
Definition of Machine Learning (Mitchell 1997) — “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 the tasks
improves with the experiences”
Spam Mail detection learning problem
1. Task T: To recognize and classify emails into ‘spam’ or ‘not spam’.
2. Performance measure P: Total percent of mails being correctly
classified as ‘spam’ (or ‘not spam’ ) by the program.
3. Training experience E: A set of mails with given labels (‘spam’ /
‘not spam’).
Simple learning process
For any learning system, we must be knowing the three elements — T
(Task), P (Performance Measure), and E (Training Experience). At a
high level, the process of learning system looks as below.
,The goal of the learning process is to find the final hypothesis that best
approximates the unknown target function.
Design of a learning system
1. Type of training experience
2. The exact type of knowledge to be learned (Choosing the Target
Function). Initially, the target function will be unknown.
3. A representation for this target knowledge (Choosing a representation
for the Target Function)
4. A learning mechanism (Choosing an approximation algorithm for the
Target Function)
, 1. Task T: To play checkers
2. Performance measure P: Total percent of the game won in the
tournament.
3. Training experience E: A set of games played against itself
Training experience
1. Direct or Indirect training experience
Direct : An individual board states and correct move for each board
state are given.
Indirect: The move sequences for a game and the final result (win,
loss or draw) are given for a number of games.
2. Supervised / Unsupervised:
Supervised: board states will be labeled with the correct move.
Unsupervised: The training experience will be unlabeled, which
means, all the board states will not have the moves. So the learner
generates random games and plays against itself with no
supervision.
3. Performance
