EC25401 Artificial Intelligence and Machine Learning – Semester IV – ECE – R-2025

Subject Code & Name: EC25401 – Artificial Intelligence and Machine Learning

Regulation: R-2025

Semester: IV (Fourth Semester)

Branch: B.E. Electronics and Communication Engineering (ECE)

Credits / L-T-P: 3 Credits | L-T-P: 2-0-2

Course Objectives

• To provide foundational AI/ML knowledge covering core techniques and enable real- world application using modern tools and frameworks.

Full Unit-wise Syllabus

Unit I – Introduction To AI

Artificial Intelligence: Introduction, Applications, Problem types. Problem formulation, Intelligent Agents: Types, Architectures, and Environments, PEAS framework. Reasoning and Logic: Propositional Logic, First-order Logic, Inference techniques including Forward and Backward Chaining

Unit II – Search Strategies

Solving problems by searching, Design issues in search algorithms. Uninformed Search: BFS, DFS. Heuristic Search: Generate-and-Test, Hill Climbing, Best-First Search, A*, Alpha-Beta pruning. Advanced methods: Problem Reduction, AO* Algorithm, Constraint Satisfaction, Means-Ends Analysis.

Unit III – Artificial Neural Networks

Introduction to Neural Networks, Activation Functions, Optimization Algorithms: Gradient Descent. Architectures: Perceptrons, Adaline, Multilayer Perceptrons. Training: Backpropagation, Procedures, Network Tuning.

Unit IV – Machine Learning Basics

Machine Learning: Basics, Applications. Comparison with Data Mining and Big Data Analytics. Supervised Learning: Naive Bayes, KNN, Decision Trees. Unsupervised Learning: k-means clustering. Introduction to Reinforcement Learning.

Unit V – Forecasting, Advanced Learning & Ensemble Methods

Forecasting: Non-linear and Logistic Regression, Random Forests, Bayesian Networks, Bias-Variance Tradeoff, Tuning and Model Selection. Clustering: EM Algorithm, Hierarchical Clustering, Post-Clustering Supervised Learning. Kernel Methods: SVM, Soft Margins, Kernel Trick. Ensemble Methods: Bagging, Boosting, Stacking, AdaBoost, Cross-Validation. Dimensionality Reduction: PCA, LDA, MDS, Feature Selection Techniques.

List of Suggested Experiments

• Implementation of a simple intelligent agent simulation
• Solving AI problems using BFS and DFS algorithms
• Implementing heuristic search strategies (A*, Hill Climbing)
• Design and train a perceptron for basic classification
• Building a multi-layer neural network using backpropagation
• Data preprocessing and feature selection for ML tasks
• Apply supervised learning algorithms (Naive Bayes, Decision Tree, KNN)
• Unsupervised learning using k-means and Apriori algorithms
• Regression techniques and Random Forest model implementation
• Dimensionality reduction using PCA and LDA with visualization

Course Outcomes (COs)

• CO1: Explain fundamental concepts of control systems and model physical systems using transfer functions, block diagrams, and signal flow graphs.
• CO2: Analyze time domain response, steady-state error, and system performance using standard test signals and specifications.
• CO3: Analyze system stability using Routh-Hurwitz criterion, root locus, and frequency domain techniques like Bode and Nyquist plots.
• CO4: Design controllers and compensators and apply state-space techniques for modern control system analysis and design.
• Note: the official PDF prints these Control Systems course outcomes under EC25401 (they appear to be copied from EC25C12); they are reproduced here exactly as printed.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Quiz (5%), Project (15%), Assignment Programs (25%), Practical (25%), Internal Examinations (30%)

Source: Official Anna University – B.E. Electronics and Communication Engineering R-2025 Syllabus
Last Updated: October 2026