ARTIFICIAL INTELLIGENCE
&
MACHINE LEARNING
Lecture Notes
B.TECH
(III YEAR – II SEM)
(2022-2023)
Prepared by:
Ms.Anitha Patibandla, Associate Professor
Dr.B.Jyothi, Professor
Ms.K.Bhavana, Assistant Professor
MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY
(Autonomous Institution – UGC, Govt. of India)
Department of Electronics and Communication Engineering
Recognized under 2(f) and 12 (B) of UGC ACT 1956
(Affiliated to JNTUH, Hyderabad, Approved by AICTE-Accredited by NBA &NAAC–‘A’Grade-ISO9001:2015
Certified) Maisammaguda,Dhulapally(PostVia.Kompally),Secunderabad–500100,Telangana State, India
, MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY
III Year B.Tech. ECE- I Sem L/T/P/C
3/-/-/3
(R20A05XXX) ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
COURSE OBJECTIVES:
1. To train the students to understand different types of AI agents.
2. To understand various AI search algorithms.
3. Fundamentals of knowledge representation, building of simple knowledge-based
systems and to apply knowledge representation.
4. To introduce the basic concepts and techniques of machine learning and the need for
Machine learning techniques for real world problem
5. To provide understanding of various Machine learning algorithms and the way to
evaluate the performance of ML algorithms
UNIT - I:
Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving
Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth-First, Depth-First
Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic Best-First,
A*), Constraint Satisfaction (Backtracking, Local Search)
UNIT - II:
Advanced Search: Constructing Search Trees, Stochastic Search, AO* Search Implementation,
Minimax Search, Alpha-Beta Pruning Basic Knowledge Representation and Reasoning:
Propositional Logic, First-Order Logic, Forward Chaining and Backward Chaining, Introduction to
Probabilistic Reasoning, Bayes Theorem
UNIT - III:
Machine-Learning : Introduction. Machine Learning Systems, Forms of Learning: Supervised
and Unsupervised Learning, reinforcement – theory of learning – feasibility of learning – Data
Preparation– training versus testing and split.
UNIT - IV:
Supervised Learning:
Regression: Linear Regression, multi linear regression, Polynomial Regression, logistic
regression, Non-linear Regression, Model evaluation methods. Classification: – support vector
machines ( SVM) , Naïve Bayes classification
,UNIT - V:
Unsupervised learning
Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical
clustering – k-d trees ,Clustering trees – learning ordered rule lists – learning unordered rule .
Reinforcement learning- Example: Getting Lost -State and Action Spaces
TEXT BOOKS:
1. Russell, S. and Norvig, P, Artificial Intelligence: A Modern Approach, Third Edition,
PrenticeHall, 2010.
2. MACHINE LEARNING An Algorithmic Perspective 2nd Edition,Stephen Marsland,2015, by
Taylor & Francis Group, LLC
3.Introduction to Machine Learning ,The Wikipedia Guide
REFERENCES:
1. Artificial Intelligence, Elaine Rich, Kevin Knight, Shivasankar B. Nair, The McGraw Hill
publications, Third Edition, 2009. 2. George F. Luger,
2. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson
Education, 6th ed., 2009.
3. Introduction to Machine Learning, Second Edition, Ethem Alpaydın, the MIT Press,
Cambridge, Massachusetts, London, England.
4. Machine Learning , Tom M. Mitchell, McGraw-Hill Science, ISBN: 0070428077
5. Understanding Machine Learning:From Theory to Algorithms, c 2014 by ShaiShalev-
Shwartz and Shai Ben-David, Published 2014 by Cambridge University Press.
COURSE OUTCOMES:
1. Understand the informed and uninformed problem types and apply search strategies to
solve them.
2. Apply difficult real life problems in a state space representation so as to solve those
using AI techniques like searching and game playing.
3. Apply machine learning techniques in the design of computer systems
4. To differentiate between various categories of ML algorithms
5. Design and make modifications to existing machine learning algorithms.
, UNIT- I
Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving
Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth First, Depth-
First Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic
Best-First, A*), Constraint Satisfaction (Backtracking, Local Search)
Introduction:
Artificial Intelligence is concerned with the design of intelligence in an artificial device.
The term was coined by John McCarthy in 1956.
Intelligence is the ability to acquire, understand and apply the knowledge to achieve
goalsin the world.
AI is the study of the mental faculties through the use of computational models
AI is the study of intellectual/mental processes as computational processes.
AI program will demonstrate a high level of intelligence to a degree that equals
orexceeds the intelligence required of a human in performing some task.
AI is unique, sharing borders with Mathematics, Computer Science,
Philosophy, Psychology, Biology, Cognitive Science and many others.
Although there is no clear definition of AI or even Intelligence, it can be described as an
attempt to build machines that like humans can think and act, able to learn and use
knowledge to solve problems on their own.
Sub Areas of AI:
1) Game Playing
Deep Blue Chess program beat world champion Gary Kasparov
2) Speech Recognition
PEGASUS spoken language interface to American Airlines' EAASY SABRE reservation
system, which allows users to obtain flight information and make reservations over the
1
&
MACHINE LEARNING
Lecture Notes
B.TECH
(III YEAR – II SEM)
(2022-2023)
Prepared by:
Ms.Anitha Patibandla, Associate Professor
Dr.B.Jyothi, Professor
Ms.K.Bhavana, Assistant Professor
MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY
(Autonomous Institution – UGC, Govt. of India)
Department of Electronics and Communication Engineering
Recognized under 2(f) and 12 (B) of UGC ACT 1956
(Affiliated to JNTUH, Hyderabad, Approved by AICTE-Accredited by NBA &NAAC–‘A’Grade-ISO9001:2015
Certified) Maisammaguda,Dhulapally(PostVia.Kompally),Secunderabad–500100,Telangana State, India
, MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY
III Year B.Tech. ECE- I Sem L/T/P/C
3/-/-/3
(R20A05XXX) ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
COURSE OBJECTIVES:
1. To train the students to understand different types of AI agents.
2. To understand various AI search algorithms.
3. Fundamentals of knowledge representation, building of simple knowledge-based
systems and to apply knowledge representation.
4. To introduce the basic concepts and techniques of machine learning and the need for
Machine learning techniques for real world problem
5. To provide understanding of various Machine learning algorithms and the way to
evaluate the performance of ML algorithms
UNIT - I:
Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving
Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth-First, Depth-First
Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic Best-First,
A*), Constraint Satisfaction (Backtracking, Local Search)
UNIT - II:
Advanced Search: Constructing Search Trees, Stochastic Search, AO* Search Implementation,
Minimax Search, Alpha-Beta Pruning Basic Knowledge Representation and Reasoning:
Propositional Logic, First-Order Logic, Forward Chaining and Backward Chaining, Introduction to
Probabilistic Reasoning, Bayes Theorem
UNIT - III:
Machine-Learning : Introduction. Machine Learning Systems, Forms of Learning: Supervised
and Unsupervised Learning, reinforcement – theory of learning – feasibility of learning – Data
Preparation– training versus testing and split.
UNIT - IV:
Supervised Learning:
Regression: Linear Regression, multi linear regression, Polynomial Regression, logistic
regression, Non-linear Regression, Model evaluation methods. Classification: – support vector
machines ( SVM) , Naïve Bayes classification
,UNIT - V:
Unsupervised learning
Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical
clustering – k-d trees ,Clustering trees – learning ordered rule lists – learning unordered rule .
Reinforcement learning- Example: Getting Lost -State and Action Spaces
TEXT BOOKS:
1. Russell, S. and Norvig, P, Artificial Intelligence: A Modern Approach, Third Edition,
PrenticeHall, 2010.
2. MACHINE LEARNING An Algorithmic Perspective 2nd Edition,Stephen Marsland,2015, by
Taylor & Francis Group, LLC
3.Introduction to Machine Learning ,The Wikipedia Guide
REFERENCES:
1. Artificial Intelligence, Elaine Rich, Kevin Knight, Shivasankar B. Nair, The McGraw Hill
publications, Third Edition, 2009. 2. George F. Luger,
2. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson
Education, 6th ed., 2009.
3. Introduction to Machine Learning, Second Edition, Ethem Alpaydın, the MIT Press,
Cambridge, Massachusetts, London, England.
4. Machine Learning , Tom M. Mitchell, McGraw-Hill Science, ISBN: 0070428077
5. Understanding Machine Learning:From Theory to Algorithms, c 2014 by ShaiShalev-
Shwartz and Shai Ben-David, Published 2014 by Cambridge University Press.
COURSE OUTCOMES:
1. Understand the informed and uninformed problem types and apply search strategies to
solve them.
2. Apply difficult real life problems in a state space representation so as to solve those
using AI techniques like searching and game playing.
3. Apply machine learning techniques in the design of computer systems
4. To differentiate between various categories of ML algorithms
5. Design and make modifications to existing machine learning algorithms.
, UNIT- I
Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving
Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth First, Depth-
First Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic
Best-First, A*), Constraint Satisfaction (Backtracking, Local Search)
Introduction:
Artificial Intelligence is concerned with the design of intelligence in an artificial device.
The term was coined by John McCarthy in 1956.
Intelligence is the ability to acquire, understand and apply the knowledge to achieve
goalsin the world.
AI is the study of the mental faculties through the use of computational models
AI is the study of intellectual/mental processes as computational processes.
AI program will demonstrate a high level of intelligence to a degree that equals
orexceeds the intelligence required of a human in performing some task.
AI is unique, sharing borders with Mathematics, Computer Science,
Philosophy, Psychology, Biology, Cognitive Science and many others.
Although there is no clear definition of AI or even Intelligence, it can be described as an
attempt to build machines that like humans can think and act, able to learn and use
knowledge to solve problems on their own.
Sub Areas of AI:
1) Game Playing
Deep Blue Chess program beat world champion Gary Kasparov
2) Speech Recognition
PEGASUS spoken language interface to American Airlines' EAASY SABRE reservation
system, which allows users to obtain flight information and make reservations over the
1
