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JNTUA College of Engineering (Autonomous), Ananthapuramu
Department of Computer Science & Engineering
Introduction to AI/ML
Course Code: MINOR DEGREE (R20) L T P C : 3 1 0 4
Course Objectives:
● AI programming focuses on three cognitive skills learning, reasoning and self-correction.
● AI is a research field that studies how to realize the intelligent human behaviors on a computer.
● Understand the basic theory underlying machine learning
● Formulate machine learning problems corresponding to different applications.
Course Outcomes:
After completion of the course, students will be able to
CO1: Solve basic AI based problems.
CO2: Define the concept of Artificial Intelligence.
CO3: Apply AI techniques to real-world problems to develop intelligent systems.
CO4: Identify machine learning techniques suitable for a given problem.
CO5: Solve the real world problems using various machine learning techniques.

UNIT – I: Fundamentals of AI
Introduction: What is AI, Foundations of AI, History of AI, The State of Art.
Intelligent Agents: Agents and Environments, Good Behaviour: The Concept of Rationality, The
Nature of Environments, The Structure of Agents.
UNIT – II: Solving Problems by searching
Problem Solving Agents, Example problems, Searching for Solutions, Uninformed Search Strategies,
Informed search strategies, Heuristic Functions, Beyond Classical Search: Local Search Algorithms
and Optimization Problems, Local Search in Continues Spaces, Searching with Nondeterministic
Actions, Searching with partial observations, online search agents and unknown environments.
UNIT – III: Reinforcement Learning
Introduction, Passive Reinforcement Learning, Active Reinforcement Learning, Generalization in
Reinforcement Learning, Policy Search, applications of RL
Natural Language Processing: Language Models, Text Classification, Information Retrieval,
Information Extraction.
UNIT – IV: Introduction
Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate
Eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space
Search.
UNIT – V: 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 Evolution and Learning.

Textbooks:
rd
1. Stuart J.Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, 3 Edition,
Pearson Education, 2019.

2. T.M. Mitchell, “Machine Learning”, McGraw-Hill, 1997.

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