Annexure-II 2
Artificial Intelligence and Machine Learning Semester 6
Course Code BDS602 CIE Marks 50
Teaching Hours/Week (L: T:P: S) 4:0:0:0 SEE Marks 50
Total Hours of Pedagogy 50 Total Marks 100
Credits 04 Exam Hours 03
Examination type (SEE) Theory
Course objectives:
● Understands the basics of AI, history of AI and its foundations, basic principles of
AI for problem solving
● Explore the basics of Machine Learning & Machine Learning process,
understanding data,
● Understand the Working of Artificial Neural Networks
Teaching-Learning Process (General Instructions)
These are sample Strategies, which teachers can use to accelerate the attainment of the
various course outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5.Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills,
develop design thinking skills such as the ability to design, evaluate, generalize, and
analyze information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible,
it helps improve the students' understanding.
Module-1
Introduction: What is AI, The foundation of Artificial Intelligence, The history of Artificial
Intelligence, Intelligent Agents: Agents and Environments, Good Behaviour: The concept of
rationality, the nature of Environments, the structure of Agents.
Textbook 1: Chapter: 1 and 2
Module-2
Problem solving by searching: Problem solving agents, Example problems, Searching for
solutions, Uniformed search strategies, Informed search strategies, Heuristic functions
Textbook 1: Chapter: 3
Module-3
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Introduction to machine learning: Need for Machine Learning, Machine Learning
Explained, and Machine Learning in relation to other fields, Types of Machine Learning.
Challenges of Machine Learning, Machine Learning process, Machine Learning
applications.
Understanding Data: What is data, types of data, Big data analytics and types of analytics,
Big data analytics framework, Descriptive statistics, univariate data analysis and
visualization
Textbook 2: Chapter: 1 and 2.1 to 2.5
Module-4
Understanding Data Bivariate and Multivariate data, Multivariate statistics , Essential
mathematics for Multivariate data, Overview hypothesis, Feature engineering and
dimensionality reduction techniques.
Basics of Learning Theory: Introduction to learning and its types, Introduction computation
learning theory, Design of learning system, Introduction concept learning.
Similarity-based learning: Introduction to Similarity or instance based learning, Nearest-
neighbour learning, weighted k- Nearest - Neighbour algorithm.
Textbook 2: Chapter: 2.6 to 2.10, 3.1 to 3.4, 4.1 to 4.3
Module-5
Artificial Neural Network: Introduction, Biological neurons, Artificial neurons, Perceptron
and learning theory, types of Artificial neural Network, learning in multilayer Perceptron,
Radial basis function neural network, self-organizing feature map,
Textbook 2: Chapter: 10
Course outcome (Course Skill Set)
At the end of the course, the student will be able to :
1. Explain Basics of Artificial Intelligence.
2. Apply the suitable search strategy to solve problems.
3. Develop similarity-based learning models and regression models for solving classification
and prediction tasks.
4. Utilize probabilistic learning models & clustering algorithms to identify patterns in data and
implement reinforcement learning techniques.
5. Build neural network models using perceptrons and multilayer architectures.
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Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks out of 50) and for the
SEE minimum passing mark is 35% of the maximum marks (18 out of 50 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/ course
if the student secures a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous
Internal Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
● For the Assignment component of the CIE, there are 25 marks and for the Internal
Assessment Test component, there are 25 marks.
● The first test will be administered after 40-50% of the syllabus has been covered, and the
second test will be administered after 85-90% of the syllabus has been covered
● Any two assignment methods mentioned in the 22OB2.4, if an assignment is project-based
then only one assignment for the course shall be planned. The teacher should not conduct
two assignments at the end of the semester if two assignments are planned.
● For the course, CIE marks will be based on a scaled-down sum of two tests and other
methods of assessment.
Internal Assessment Test question paper is designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester-End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common
question papers for the course (duration 03 hours).
1. The question paper will have ten questions. Each question is set for 20 marks.
2. There will be 2 questions from each module. Each of the two questions under a module
(with a maximum of 3 sub-questions), should have a mix of topics under that module.
3. The students have to answer 5 full questions, selecting one full question from each
module.
4. Marks scored shall be proportionally reduced to 50 marks.
Suggested Learning Resources:
Textbooks:
1. Stuart Russel, Peter Norvig: “Artificial Intelligence A Modern Approach”, 3rd Edition,
Pearson Education, 2015.
2. S. Sridhar, M Vijayalakshmi “Machine Learning”. Oxford University Press, 2021.
REFERENCE BOOKS:
1. Elaine Rich, Kevin Knight: “Artificial Intelligence”, 3rd Edition, Tata McGraw Hill, 2009,
ISBN-10: 0070087709
2. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, 1980, ISBN: 978-3-540-
11340-9
3. Murty, M. N., and V. S. Ananthanarayana. Machine Learning: Theory and Practice,
Universities Press, 2024.
Web links and Video Lectures (e-Resources):
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Weblinks and Video Lectures (e-Resources):
1. Problem solving agent:https://www.youtube.com/watch?v=KTPmo-KsOis
2.https://www.youtube.com/watch?v=X_Qt0U66aH0&list=PLwdnzlV3ogoXaceHrrFVZCJKb
m_laSH cH
3. https://www.javatpoint.com/history-of-artificial-intelligence
4. https://www.tutorialandexample.com/problem-solving-in-artificial-intelligence
5. https://techvidvan.com/tutorials/ai-heuristic-search/
6. https://www.analyticsvidhya.com/machine-learning/
7. https://www.hackerearth.com/practice/machine-learning/machine-learning-
algorithms/mldecision-tree/tutorial/
8. https://www.javatpoint.com/unsupervised-artificial-neural-networks
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
Course project/Programming assignment: Real-world problem solving related to AI and ML [25
marks]
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