K L Deemed to be University
Department of Electronics and Communication Engineering -- KLVZA
Course Handout
2023-2024, Odd Sem
Course Title :DATA DRIVEN ARTIFICIAL INTELLIGENT SYSTEMS
Course Code :22AD2001R
L-T-P-S Structure : 2-0-2-0
Pre-requisite :
Credits :3
Course Coordinator :Imran Rasheed
Team of Instructors :
Teaching Associates :
Syllabus :Module-1: Foundations of Artificial Intelligence, Intelligent agents, their environments, heuristic search techniques, including A* search
and other best-first search algorithms, Constraint Satisfaction and Reasoning, solve constraint satisfaction problems using backtracking, forward
checking, and other methods, knowledge representation techniques, such as propositional and first order logic. Module-2: Probabilistic reasoning for
AI, including Bayesian networks and inference algorithms, Machine Learning and Neural Networks: machine learning algorithms, such as supervised
and unsupervised learning techniques, and how to pre-process and analyse data, Find S, Concept learning search and Candidate Elimination
Algorithm (CEA), evaluating a hypothesis, probably learning approximately correct hypothesis, and function approximation. Module-3: Artificial
Neural Networks (ANN), including the structure and functionality of feedforward and recurrent networks. Architecture, learning and inference.
Performance measures. Convolutional Neural Networks (CNN) and Deep Learning techniques for tasks like image recognition, natural language
processing, and reinforcement learning. Module-4: Data Science and Analytics: This module focuses on the essentials of data science, including data
classification, analytics, visualization, and processing techniques. various data science algorithms, such as decision trees, k-means clustering, and
principal component analysis, Linear Regression, Logistic Regression, Decision Trees different types of data analytics, including descriptive,
diagnostic, predictive, and prescriptive analytics, and understand how they can be applied to real-world problems.
Text Books :Book 1: Artificial Intelligence Russel and Norvig 3rd Edition Pearson Education, PHI, (2015) Book 2: Artificial Intelligence Elaine Rich
& Kevin Knight 3rd Edition Tata McGraw-Hill Edition, Reprint (2008) Book 3 Data science Handbook Field cady Wiley
Reference Books :Book 1 Artificial Intelligence Patrick Henry Winston 3rd Edition Pearson Education (2003) Book 2 Introducing Data science Davy
Cielen 3rd Edition Manning (2016)
Web Links :https://www.javatpoint.com/artificial-intelligence-ai https://www.w3schools.com/ai/default.asp
https://www.tutorialspoint.com/artificial_intelligence/index.htm https://www.geeksforgeeks.org/introduction-to-data-science/
https://www.w3schools.com/datascience/ds_introduction.asp
MOOCS :MOOCs Resource 1: What is Data Science? https://www.coursera.org/programs/cse-faculty-courses-an6zm/data-science/machine-
learning?productId=r0GnHOZaEees-Q6jQMxlrg&productType=course&showMiniModal=true MOOCs Resource 2: Introduction to Machine
Learning https://www.coursera.org/programs/cse-faculty-courses-an6zm/skills/machine-learning?collectionId=skill~machine-
learning&productId=8BJHzZD_EeiKohJBs5wRGA&productType=course&showMiniModal=true&source=browse MOOCs Resource 3: Introduction
to Artificial Intelligence (AI) https://www.coursera.org/programs/cse-faculty-courses-an6zm/data-science/machine-learning?
productId=mR7MlUaTEemuHQ4HpHozrA&productType=course&showMiniModal=true MOOCs Resource 4: Introduction to Data Science in
Python https://www.coursera.org/programs/cse-faculty-courses-an6zm/browse?
collectionId=&productId=RZ1S0B0MEeacvQ6cODzg5Q&productType=course&query=data+science+course&showMiniModal=true&source=search
Course Rationale :AI for Data Science is about the discipline needed to analyze high quality software data that can be understood, maintained and
adapted over long period of time by many different people. In order,to enable the student to analyze quality data, the course provides an overview of
the Artificial Intelligence and Data Science discipline, introducing the fundamental principles and methods in AI and Data Science and highlights the
need for an engineering approach to analyze the problem by visualization. It provides an opportunity for the students to gain knowledge of industrial
approach to real- world projects and importance of team environment. The course covers various methods and models to train the student to learn the
process classification, partitioning and clustering and design models based on the analysis.
Course Objectives :The emphasis of this course is to learn about fundamentals of AI concepts including several search algorithms, Machine
Learning and Neural Networks, Deep learning techniques as well as data science algorithms, analytics techniques, and data visualization methods to
process, analyse and effectively communicate insights from complex datasets.
COURSE OUTCOMES (COs):
Blooms
CO Taxonomy
Course Outcome (CO) PO/PSO
NO Level
(BTL)
Understand and apply the concepts of intelligent agents and various search algorithms, to
CO1 PSO2,PO1,PO2 3
solve real-world problems.
Analyse satisfaction problems, discover knowledge using logic, and analyse reasoning
CO2 PSO1,PO2,PO3 4
techniques to make informed decisions in uncertain environments.
Apply and analyse various Machine Learning algorithms, Examine CNN and Deep Learning
CO3 PSO2,PO1,PO3 4
techniques
Apply various Data Visualization Techniques, Analyse Data analytics techniques, Discover
CO4 PO5,PSO2,PO3 4
the insights from complex datasets.
CO5 Examine AI for Data science lab in the python environment. PSO1,PO2,PO3 5
COURSE OUTCOME INDICATORS (COIs)::
,Outcome Highest
COI-2 COI-3 COI-4 COI-5
No. BTL
Btl-2 Btl-3
Understand fundamental AI Apply various search
CO1 3
concepts including intelligent algorithms to solve real-world
agents. problems.
Btl-2 Btl-3 Btl-4
Describe constraint satisfaction Solve reasoning techniques to Analyze the local and
CO2 4
problems, represent knowledge make informed decisions in adversarial search for Optimal
using logic. uncertain environments. decisions.
Btl-2
Understand various machine Btl-3 Btl-4
CO3 4 learning algorithms, including Apply different ANN and deep Analyze data and make
artificial neural networks and learning techniques predictions
deep learning techniques
Btl-3 Btl-4
Btl-2
Illustrate various analytics Analyze the insights
CO4 4 Summarize various data
techniques and data effectively from complex
science algorithms
visualization methods datasets
Btl-5
CO5 5 Develop AI for Data Science
lab in the python environment.
PROGRAM OUTCOMES & PROGRAM SPECIFIC OUTCOMES (POs/PSOs)
Po
Program Outcome
No.
Engineering Knowledge:Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the
PO1
solution of complex engineering problems.
Problem Analysis: Identify, formulate, review research literature, and analyse complex engineering problems reaching substantiated
PO2
conclusions using first principles of mathematics, natural sciences and engineering sciences
Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet
PO3 the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental
considerations
Conduct Investigations of Complex Problems:Use research-based knowledge and research methods including design of experiments, analysis
PO4 and interpretation of data, and synthesis of the information to provide valid conclusions for complex problems that cannot be solved by
straightforward application of knowledge, theories and techniques applicable to the engineering discipline.
Modern Tool Usage:Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction
PO5
and modelling to complex engineering activities with an understanding of the limitations.
The Engineer and Society:Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues
PO6
and the consequent responsibilities relevant to the professional engineering practice.
Environment and Sustainability:Understand the impact of the professional engineering solutions in societal and environmental contexts, and
PO7
demonstrate the knowledge of, and need for sustainable development
PO8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice
Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary
PO9
settings.
Communication:Communicate effectively on complex engineering activities with the engineering community and with society at large, such
PO10 as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear
instructions
Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these
PO11
to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and lifelong learning in the
PO12
broadest context of technological change.
An ability to solve Electronics engineering problems, using latest hardware and software tools, to obtain appropriate solutions in the domain
PSO1
of embedded systems and Internet of things.
PSO2 Ability to design web applications by applying the knowledge of cyber security.
Lecture Course DELIVERY Plan:
Teaching-
Book No[CH No]
Sess.No. CO COI Topic Learning EvaluationComponents
[Page No]
Methods
ALM,End Semester Exam,Home
COI- Assignment,Lab Weekly
1 CO1 Handout Discussion Text book 1 Chalk,PPT,Talk
2 exercise,MOOCs Review,SEM-
EXAM1
Introduction to AI and Data Science, Intelligent
ALM,End Semester Exam,Home
Agents, Overview of various AI, Data Science
COI- T BOOK[1],CH 2.1- Assignment,Lab Weekly
2 CO1 applications across domains (healthcare, Chalk,PPT,Talk
2 2.3, Pageno 30-49" exercise,MOOCs Review,SEM-
agriculture, robotics, automation, law, medicine,
EXAM1
pharmacy, and business management)
, Teaching-
Book No[CH No]
Sess.No. CO COI Topic Learning EvaluationComponents
[Page No]
Methods
ALM,End Semester Exam,Home
COI- T BOOK[1],CH 2.1- Assignment,Lab Weekly
3 CO1 Best-First Search, Introduction to Heuristics Chalk,PPT,Talk
2 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Heuristic search techniques: A* Search, Hill T BOOK[1],CH2.1- Assignment,Lab Weekly
4 CO1 Chalk,PPT,Talk
3 Climbing 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
Introduction to Constraint Satisfaction Problems T BOOK[1],
COI- Assignment,Lab Weekly
5 CO1 (CSP), Techniques for solving CSP (Backtracking, CH2.6,2.8,Page no56- Chalk,PPT,Talk
3 exercise,MOOCs Review,SEM-
Forward Checking) 61
EXAM1
ALM,End Semester Exam,Home
Knowledge representation techniques (Semantic
COI- T BOOK[1],CH29.6, Assignment,Lab Weekly
6 CO1 Networks, Frames, Propositional and First-Order Chalk,PPT,Talk
3 Pageno 772-776 exercise,MOOCs Review,SEM-
Logic)
EXAM1
ALM,End Semester Exam,Home
COI- Reasoning techniques (Forward and Backward T BOOK[1],CH29.6,
7 CO1 Chalk,PPT,Talk Assignment,MOOCs
2 Chaining, Resolution) (flipped learning) Pageno 772-776
Review,SEM-EXAM1
ALM,End Semester Exam,Home
COI- Probabilistic Reasoning for AI, Introduction to BOOK[1], CH1.3-1.6, Assignment,Lab Weekly
8 CO2 Chalk,PPT,Talk
2 Probability Theory Pageno 12-23" exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
Introduction to Bayesian Networks,Inference in
COI- "T BOOK[1], CH2.1- Assignment,Lab Weekly
9 CO2 Bayesian Networks (Exact and Approximate Chalk,PPT,Talk
2 2.3,Page no30-49" exercise,MOOCs Review,SEM-
Inference)
EXAM1
ALM,End Semester Exam,Home
COI- Introduction to Machine Learning (ML), "T BOOK [1], CH Assignment,Lab Weekly
10 CO2 Chalk,PPT,Talk
3 preprocess and analyze data, 2.4,2.5, Page no 50-55" exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Supervised, Unsupervised, and Reinforcement T BOOK[1],CH 2.1- Assignment,Lab Weekly
11 CO2 Chalk,PPT,Talk
2 Learning (flipped learning) 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Find S Algorithm, Concept learning search, Assignment,Lab Weekly
12 CO2 T BOOK[1], CH1.3- Chalk,PPT,Talk
4 Candidate Elimination Algorithm (CEA) exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
evaluating a hypothesis, probably learning
COI- "T BOOK[3],CH Assignment,Lab Weekly
13 CO2 approximately correct hypothesis, and function Chalk,PPT,Talk
4 8,Page no165-170" exercise,MOOCs Review,SEM-
approximation.
EXAM1
ALM,End Semester Exam,Home
COI- Introduction to ANN, Perceptron, Multi-layer "T BOOK [1],CH 29.6, Assignment,Lab Weekly
14 CO3 Chalk,PPT,Talk
4 Perceptron Page no 772-776" exercise,MOOCs Review,SEM-
EXAM2
ALM,End Semester Exam,Home
COI- T BOOK [1],CH 29.6, Assignment,Lab Weekly
15 CO3 Backpropagation Chalk,PPT,Talk
2 Page no 772- 776 exercise,MOOCs Review,SEM-
EXAM2
ALM,End Semester Exam,Home
COI- "T BOOK [1],CH 29.6, Assignment,Lab Weekly
16 CO3 Introduction to CNN and DL Chalk,PPT,Talk
4 Page no 772-776" exercise,MOOCs Review,SEM-
EXAM2
17 CO3 COI- Applications of CNN in Image Recognition and "T BOOK[1],CH29.6, Chalk,PPT,Talk ALM,End Semester Exam,Home
4 Processing Page no 772-776" Assignment,Lab Weekly
Department of Electronics and Communication Engineering -- KLVZA
Course Handout
2023-2024, Odd Sem
Course Title :DATA DRIVEN ARTIFICIAL INTELLIGENT SYSTEMS
Course Code :22AD2001R
L-T-P-S Structure : 2-0-2-0
Pre-requisite :
Credits :3
Course Coordinator :Imran Rasheed
Team of Instructors :
Teaching Associates :
Syllabus :Module-1: Foundations of Artificial Intelligence, Intelligent agents, their environments, heuristic search techniques, including A* search
and other best-first search algorithms, Constraint Satisfaction and Reasoning, solve constraint satisfaction problems using backtracking, forward
checking, and other methods, knowledge representation techniques, such as propositional and first order logic. Module-2: Probabilistic reasoning for
AI, including Bayesian networks and inference algorithms, Machine Learning and Neural Networks: machine learning algorithms, such as supervised
and unsupervised learning techniques, and how to pre-process and analyse data, Find S, Concept learning search and Candidate Elimination
Algorithm (CEA), evaluating a hypothesis, probably learning approximately correct hypothesis, and function approximation. Module-3: Artificial
Neural Networks (ANN), including the structure and functionality of feedforward and recurrent networks. Architecture, learning and inference.
Performance measures. Convolutional Neural Networks (CNN) and Deep Learning techniques for tasks like image recognition, natural language
processing, and reinforcement learning. Module-4: Data Science and Analytics: This module focuses on the essentials of data science, including data
classification, analytics, visualization, and processing techniques. various data science algorithms, such as decision trees, k-means clustering, and
principal component analysis, Linear Regression, Logistic Regression, Decision Trees different types of data analytics, including descriptive,
diagnostic, predictive, and prescriptive analytics, and understand how they can be applied to real-world problems.
Text Books :Book 1: Artificial Intelligence Russel and Norvig 3rd Edition Pearson Education, PHI, (2015) Book 2: Artificial Intelligence Elaine Rich
& Kevin Knight 3rd Edition Tata McGraw-Hill Edition, Reprint (2008) Book 3 Data science Handbook Field cady Wiley
Reference Books :Book 1 Artificial Intelligence Patrick Henry Winston 3rd Edition Pearson Education (2003) Book 2 Introducing Data science Davy
Cielen 3rd Edition Manning (2016)
Web Links :https://www.javatpoint.com/artificial-intelligence-ai https://www.w3schools.com/ai/default.asp
https://www.tutorialspoint.com/artificial_intelligence/index.htm https://www.geeksforgeeks.org/introduction-to-data-science/
https://www.w3schools.com/datascience/ds_introduction.asp
MOOCS :MOOCs Resource 1: What is Data Science? https://www.coursera.org/programs/cse-faculty-courses-an6zm/data-science/machine-
learning?productId=r0GnHOZaEees-Q6jQMxlrg&productType=course&showMiniModal=true MOOCs Resource 2: Introduction to Machine
Learning https://www.coursera.org/programs/cse-faculty-courses-an6zm/skills/machine-learning?collectionId=skill~machine-
learning&productId=8BJHzZD_EeiKohJBs5wRGA&productType=course&showMiniModal=true&source=browse MOOCs Resource 3: Introduction
to Artificial Intelligence (AI) https://www.coursera.org/programs/cse-faculty-courses-an6zm/data-science/machine-learning?
productId=mR7MlUaTEemuHQ4HpHozrA&productType=course&showMiniModal=true MOOCs Resource 4: Introduction to Data Science in
Python https://www.coursera.org/programs/cse-faculty-courses-an6zm/browse?
collectionId=&productId=RZ1S0B0MEeacvQ6cODzg5Q&productType=course&query=data+science+course&showMiniModal=true&source=search
Course Rationale :AI for Data Science is about the discipline needed to analyze high quality software data that can be understood, maintained and
adapted over long period of time by many different people. In order,to enable the student to analyze quality data, the course provides an overview of
the Artificial Intelligence and Data Science discipline, introducing the fundamental principles and methods in AI and Data Science and highlights the
need for an engineering approach to analyze the problem by visualization. It provides an opportunity for the students to gain knowledge of industrial
approach to real- world projects and importance of team environment. The course covers various methods and models to train the student to learn the
process classification, partitioning and clustering and design models based on the analysis.
Course Objectives :The emphasis of this course is to learn about fundamentals of AI concepts including several search algorithms, Machine
Learning and Neural Networks, Deep learning techniques as well as data science algorithms, analytics techniques, and data visualization methods to
process, analyse and effectively communicate insights from complex datasets.
COURSE OUTCOMES (COs):
Blooms
CO Taxonomy
Course Outcome (CO) PO/PSO
NO Level
(BTL)
Understand and apply the concepts of intelligent agents and various search algorithms, to
CO1 PSO2,PO1,PO2 3
solve real-world problems.
Analyse satisfaction problems, discover knowledge using logic, and analyse reasoning
CO2 PSO1,PO2,PO3 4
techniques to make informed decisions in uncertain environments.
Apply and analyse various Machine Learning algorithms, Examine CNN and Deep Learning
CO3 PSO2,PO1,PO3 4
techniques
Apply various Data Visualization Techniques, Analyse Data analytics techniques, Discover
CO4 PO5,PSO2,PO3 4
the insights from complex datasets.
CO5 Examine AI for Data science lab in the python environment. PSO1,PO2,PO3 5
COURSE OUTCOME INDICATORS (COIs)::
,Outcome Highest
COI-2 COI-3 COI-4 COI-5
No. BTL
Btl-2 Btl-3
Understand fundamental AI Apply various search
CO1 3
concepts including intelligent algorithms to solve real-world
agents. problems.
Btl-2 Btl-3 Btl-4
Describe constraint satisfaction Solve reasoning techniques to Analyze the local and
CO2 4
problems, represent knowledge make informed decisions in adversarial search for Optimal
using logic. uncertain environments. decisions.
Btl-2
Understand various machine Btl-3 Btl-4
CO3 4 learning algorithms, including Apply different ANN and deep Analyze data and make
artificial neural networks and learning techniques predictions
deep learning techniques
Btl-3 Btl-4
Btl-2
Illustrate various analytics Analyze the insights
CO4 4 Summarize various data
techniques and data effectively from complex
science algorithms
visualization methods datasets
Btl-5
CO5 5 Develop AI for Data Science
lab in the python environment.
PROGRAM OUTCOMES & PROGRAM SPECIFIC OUTCOMES (POs/PSOs)
Po
Program Outcome
No.
Engineering Knowledge:Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the
PO1
solution of complex engineering problems.
Problem Analysis: Identify, formulate, review research literature, and analyse complex engineering problems reaching substantiated
PO2
conclusions using first principles of mathematics, natural sciences and engineering sciences
Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet
PO3 the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental
considerations
Conduct Investigations of Complex Problems:Use research-based knowledge and research methods including design of experiments, analysis
PO4 and interpretation of data, and synthesis of the information to provide valid conclusions for complex problems that cannot be solved by
straightforward application of knowledge, theories and techniques applicable to the engineering discipline.
Modern Tool Usage:Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction
PO5
and modelling to complex engineering activities with an understanding of the limitations.
The Engineer and Society:Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues
PO6
and the consequent responsibilities relevant to the professional engineering practice.
Environment and Sustainability:Understand the impact of the professional engineering solutions in societal and environmental contexts, and
PO7
demonstrate the knowledge of, and need for sustainable development
PO8 Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice
Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary
PO9
settings.
Communication:Communicate effectively on complex engineering activities with the engineering community and with society at large, such
PO10 as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear
instructions
Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these
PO11
to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and lifelong learning in the
PO12
broadest context of technological change.
An ability to solve Electronics engineering problems, using latest hardware and software tools, to obtain appropriate solutions in the domain
PSO1
of embedded systems and Internet of things.
PSO2 Ability to design web applications by applying the knowledge of cyber security.
Lecture Course DELIVERY Plan:
Teaching-
Book No[CH No]
Sess.No. CO COI Topic Learning EvaluationComponents
[Page No]
Methods
ALM,End Semester Exam,Home
COI- Assignment,Lab Weekly
1 CO1 Handout Discussion Text book 1 Chalk,PPT,Talk
2 exercise,MOOCs Review,SEM-
EXAM1
Introduction to AI and Data Science, Intelligent
ALM,End Semester Exam,Home
Agents, Overview of various AI, Data Science
COI- T BOOK[1],CH 2.1- Assignment,Lab Weekly
2 CO1 applications across domains (healthcare, Chalk,PPT,Talk
2 2.3, Pageno 30-49" exercise,MOOCs Review,SEM-
agriculture, robotics, automation, law, medicine,
EXAM1
pharmacy, and business management)
, Teaching-
Book No[CH No]
Sess.No. CO COI Topic Learning EvaluationComponents
[Page No]
Methods
ALM,End Semester Exam,Home
COI- T BOOK[1],CH 2.1- Assignment,Lab Weekly
3 CO1 Best-First Search, Introduction to Heuristics Chalk,PPT,Talk
2 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Heuristic search techniques: A* Search, Hill T BOOK[1],CH2.1- Assignment,Lab Weekly
4 CO1 Chalk,PPT,Talk
3 Climbing 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
Introduction to Constraint Satisfaction Problems T BOOK[1],
COI- Assignment,Lab Weekly
5 CO1 (CSP), Techniques for solving CSP (Backtracking, CH2.6,2.8,Page no56- Chalk,PPT,Talk
3 exercise,MOOCs Review,SEM-
Forward Checking) 61
EXAM1
ALM,End Semester Exam,Home
Knowledge representation techniques (Semantic
COI- T BOOK[1],CH29.6, Assignment,Lab Weekly
6 CO1 Networks, Frames, Propositional and First-Order Chalk,PPT,Talk
3 Pageno 772-776 exercise,MOOCs Review,SEM-
Logic)
EXAM1
ALM,End Semester Exam,Home
COI- Reasoning techniques (Forward and Backward T BOOK[1],CH29.6,
7 CO1 Chalk,PPT,Talk Assignment,MOOCs
2 Chaining, Resolution) (flipped learning) Pageno 772-776
Review,SEM-EXAM1
ALM,End Semester Exam,Home
COI- Probabilistic Reasoning for AI, Introduction to BOOK[1], CH1.3-1.6, Assignment,Lab Weekly
8 CO2 Chalk,PPT,Talk
2 Probability Theory Pageno 12-23" exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
Introduction to Bayesian Networks,Inference in
COI- "T BOOK[1], CH2.1- Assignment,Lab Weekly
9 CO2 Bayesian Networks (Exact and Approximate Chalk,PPT,Talk
2 2.3,Page no30-49" exercise,MOOCs Review,SEM-
Inference)
EXAM1
ALM,End Semester Exam,Home
COI- Introduction to Machine Learning (ML), "T BOOK [1], CH Assignment,Lab Weekly
10 CO2 Chalk,PPT,Talk
3 preprocess and analyze data, 2.4,2.5, Page no 50-55" exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Supervised, Unsupervised, and Reinforcement T BOOK[1],CH 2.1- Assignment,Lab Weekly
11 CO2 Chalk,PPT,Talk
2 Learning (flipped learning) 2.3, Pageno 30-49 exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
COI- Find S Algorithm, Concept learning search, Assignment,Lab Weekly
12 CO2 T BOOK[1], CH1.3- Chalk,PPT,Talk
4 Candidate Elimination Algorithm (CEA) exercise,MOOCs Review,SEM-
EXAM1
ALM,End Semester Exam,Home
evaluating a hypothesis, probably learning
COI- "T BOOK[3],CH Assignment,Lab Weekly
13 CO2 approximately correct hypothesis, and function Chalk,PPT,Talk
4 8,Page no165-170" exercise,MOOCs Review,SEM-
approximation.
EXAM1
ALM,End Semester Exam,Home
COI- Introduction to ANN, Perceptron, Multi-layer "T BOOK [1],CH 29.6, Assignment,Lab Weekly
14 CO3 Chalk,PPT,Talk
4 Perceptron Page no 772-776" exercise,MOOCs Review,SEM-
EXAM2
ALM,End Semester Exam,Home
COI- T BOOK [1],CH 29.6, Assignment,Lab Weekly
15 CO3 Backpropagation Chalk,PPT,Talk
2 Page no 772- 776 exercise,MOOCs Review,SEM-
EXAM2
ALM,End Semester Exam,Home
COI- "T BOOK [1],CH 29.6, Assignment,Lab Weekly
16 CO3 Introduction to CNN and DL Chalk,PPT,Talk
4 Page no 772-776" exercise,MOOCs Review,SEM-
EXAM2
17 CO3 COI- Applications of CNN in Image Recognition and "T BOOK[1],CH29.6, Chalk,PPT,Talk ALM,End Semester Exam,Home
4 Processing Page no 772-776" Assignment,Lab Weekly
