ADVANCED AI AND ML
(Effective from the Academic Year 2025 - 2026)
VII SEMESTER
Course Code AM722I1A CIA Marks 50
Number of Contact Hours/Week (L: T: P: S) 3:0:2:0 SEE Marks 50
Total Hours of Pedagogy 40L + 20P Exam Hours 03
CREDITS – 4
COURSE PREREQUISITES:
● Fundamental knowledge of mathematical concepts, analytical skills and programming.
COURSE OBJECTIVES:
● Demonstrate the fundamentals of Intelligent Agents
● Illustrate the reasoning on Uncertain Knowledge
● Explore the explanation based learning in solving AI problems
● Demonstrate the applications of Rough sets and Evolutionary Computing algorithms
TEACHING - LEARNING STRATEGY:
Following are some sample strategies that can be incorporate for the Course Delivery
● Chalk and Talk Method/Blended Mode Method
● Power Point Presentation
● Expert Talk/Webinar/Seminar
● Video Streaming/Self-Study/Simulations
● Peer-to-Peer Activities
● Activity/Problem Based Learning
● Case Studies
● MOOC/NPTEL Courses
● Any other innovative initiatives with respect to the Course contents
COURSE CONTENTS
MODULE - I
IntelligentAgents: Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of 8
Environments, The Structure of Agents Problem Solving :Game Playing Hours
MODULE - II
Uncertain knowledge and Reasoning:Quantifying Uncertainty, Acting under Uncertainty , Basic Probability 8
Notation, Inference Using Full Joint Distributions, Independence , Bayes‟Rule and Its Use The
Hours
WumpusWorld Revisited
MODULE - III
Advanced Machine Learning: Overview, Gradient Descent algorithm, Scikit-learn library for ML,
Advanced Regression models, Advanced ML algorithms, KNN, ensemble methods. 8
Hours
Forecasting:Overview, components, moving average, decomposing time series, autoregressive Models.
MODULE - IV
Instance-Based and Reinforcement Learning: k-Nearest Neighbor learning, Locally Weighted Regression,
Radial Basis Function, Case-Based Reasoning, Reinforcement Learning, Learning task, Q-Learning.
8
Recommender System: Datasets, Association rules, Collaborative filtering, User-based similarity, item-based Hours
similarity, using Surprise library, Matrix factorization
Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning
MODULE - V
(Effective from the Academic Year 2025 - 2026)
VII SEMESTER
Course Code AM722I1A CIA Marks 50
Number of Contact Hours/Week (L: T: P: S) 3:0:2:0 SEE Marks 50
Total Hours of Pedagogy 40L + 20P Exam Hours 03
CREDITS – 4
COURSE PREREQUISITES:
● Fundamental knowledge of mathematical concepts, analytical skills and programming.
COURSE OBJECTIVES:
● Demonstrate the fundamentals of Intelligent Agents
● Illustrate the reasoning on Uncertain Knowledge
● Explore the explanation based learning in solving AI problems
● Demonstrate the applications of Rough sets and Evolutionary Computing algorithms
TEACHING - LEARNING STRATEGY:
Following are some sample strategies that can be incorporate for the Course Delivery
● Chalk and Talk Method/Blended Mode Method
● Power Point Presentation
● Expert Talk/Webinar/Seminar
● Video Streaming/Self-Study/Simulations
● Peer-to-Peer Activities
● Activity/Problem Based Learning
● Case Studies
● MOOC/NPTEL Courses
● Any other innovative initiatives with respect to the Course contents
COURSE CONTENTS
MODULE - I
IntelligentAgents: Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of 8
Environments, The Structure of Agents Problem Solving :Game Playing Hours
MODULE - II
Uncertain knowledge and Reasoning:Quantifying Uncertainty, Acting under Uncertainty , Basic Probability 8
Notation, Inference Using Full Joint Distributions, Independence , Bayes‟Rule and Its Use The
Hours
WumpusWorld Revisited
MODULE - III
Advanced Machine Learning: Overview, Gradient Descent algorithm, Scikit-learn library for ML,
Advanced Regression models, Advanced ML algorithms, KNN, ensemble methods. 8
Hours
Forecasting:Overview, components, moving average, decomposing time series, autoregressive Models.
MODULE - IV
Instance-Based and Reinforcement Learning: k-Nearest Neighbor learning, Locally Weighted Regression,
Radial Basis Function, Case-Based Reasoning, Reinforcement Learning, Learning task, Q-Learning.
8
Recommender System: Datasets, Association rules, Collaborative filtering, User-based similarity, item-based Hours
similarity, using Surprise library, Matrix factorization
Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning
MODULE - V
