AI & ML — BTEUP Electronics 5th Semester Syllabus 2026-27

AI & ML — What This Subject Is About

To equip students with the theoretical and practical skills needed to understand requirements, collect and pre-process data, and build and deploy machine learning and artificial intelligence solutions for real-world applications, with hands-on experience of the latest software.

Subject 5.6.1 — Open Elective-3, Option 1 (Practicum) • Qualifying (Q) • 2 Credits • L 1 – P 3 • Choose this OR IoT OR Industrial Automation & MES • 5th Semester — Electronics Engineering • Session 2026–27

What You Will Study

Section 1 — Industrial Safety Practices

Fire Extinguishers & its Types; Safely handling Tools & Equipment; Use of proper Tools & Equipment & its maintenance; OSH & practices to be observed as a precaution

Workshop safety: fire extinguishers, correct tool use and occupational safety and health.

Section 2 — Fundamentals required for AI and ML

Introduction to Artificial Intelligence and Machine Learning; Need of AI and ML; Basic Mathematical concepts related to AI and ML; Data Collection, Data Processing and Cleaning; Types of Data Sources — Structured Data, Unstructured Data, Semi Structured, Realtime Data, Open Data Set, Data Scraping; Data Acquisition Techniques — APIs (Application Programming Interfaces), Web Scrapping, Surveys and Forms, Sensor Data, Database and Data Warehouse; Data Integration — Combining Data from Multiple Sources, Data Merging, Data Aggregation; Data Pre-Processing — Data Cleaning, Outlier Detection and Removal, Duplicate Data

The foundations: what AI and ML are, the maths behind them, and where data comes from and how it is cleaned and prepared.

Section 3 — Programming concepts for AI and ML

Python Basics; Python Structure; NumPy; Pandas; Matplotlib and Seaborn; Scikit-learn

The Python toolkit for AI and ML: NumPy, Pandas, Matplotlib, Seaborn and scikit-learn.

Section 4 — Introduction to Supervised Machine Learning

Types of Machine Learning Techniques (Supervised, Unsupervised and Reinforcement Learning). Supervised Learning — Regression, Classification, Logistic Regression, Support Vector Machines, Decision Tree, Random Forest, K – Nearest Neighbors (KNN), Use cases of Regression and Classification. Introduction to Unsupervised Learning — Clustering: K – Means Clustering, Hierarchical Clustering, DBSCAN; Dimensionality Reduction; Anomaly Detection; Use cases of clustering and Dimensional Reduction; Building Machine Learning Model; Model Evaluation and Validation; Machine Learning Model Deployment

Supervised learning (regression and classification) and unsupervised learning (clustering, dimensionality reduction, anomaly detection), and how a model is built, evaluated and deployed.

Section 5 — Deep Learning, Reinforcement Learning and Natural Language Processing

Fundamentals of Deep Learning; Convolutional Neural Networks (CNNs); Recurrent Neural Networks (RNNs); Natural Language Processing; Reinforcement Learning; TensorFlow and Kera. Generative AI — Understanding of Gen AI; Applications of Gen AI; Development of Gen AI model; Integration of Gen AI in Mobile App.; Deployment

Deep learning (CNNs, RNNs), natural language processing, reinforcement learning, and generative AI — including building and deploying Gen AI models and apps.

Section 6 — Deep Learning Applications

Computer Vision; Natural Language Processing; Speech Recognition; Model Deployment

Where deep learning is used: computer vision, language, speech recognition, and deploying models.

Section 7 — Ethics and Responsibility in AI and Machine Learning

Ethics in AI and ML; Accountability and Responsibility; Privacy and Data Protection; Social Impact of AI; Ethical Guidelines

The responsible side of AI: ethics, accountability, privacy, data protection and social impact.

Note: no marks-distribution table is given for this subject in the official syllabus, so unit weightages are not shown.

Practical Work

• Load a Dataset and perform basic preprocessing.
• Splitting of Dataset into Training and Testing Set.
• Explore Python Libraries Pandas, NumPy, Matplotlib etc.
• Build Machine Learning Model for Linear Regression Analysis for predict.
• Implement K – Means clustering to segment customer based on purchasing behaviour.
• Build a Machine Learning Model – Decision Tree.

And more — the complete list of 17 practicals is inside the PDF.

Complete Syllabus

The full examination scheme, the complete practicals list and the reference books from the official syllabus. The unit-wise topics are in the "What You Will Study" section above.

Examination Scheme

Complete Practicals List

This is the complete list of practicals from the official syllabus.

• Load a Dataset and perform basic preprocessing.
• Splitting of Dataset into Training and Testing Set.
• Explore Python Libraries Pandas, NumPy, Matplotlib etc.
• Build Machine Learning Model for Linear Regression Analysis for predict.
• Implement K – Means clustering to segment customer based on purchasing behaviour.
• Build a Machine Learning Model – Decision Tree.
• Build a Machine Learning Model – Random Forest.
• Build a Machine Learning Model for Anomaly Detection.
• Build SVM model and evaluate performance (Precision, Recall, F-Score and Confusion Matrix).
• Build a Neural Network using Keras or TensorFlow to classify the handwriting digits.
• Implement a Convolutional Neural Network to classify images using given dataset.
• Build a Recurrent Neural Network (RNN) for Sentiment Analysis.
• Implement a Chatbot using RNN.
• Compare two different Gen AI models.
• Integrate a Trained Machine Learning Model in Mobile APP for Prediction.
• Case Studies.
• Mini Project.

Reference Books

Reference books are not listed for this subject in the official syllabus.

Frequently Asked Questions

How many marks is AI & ML worth in the 5th semester?
Open Elective-3: AI & ML (5.6.1) is N/A (Q) marks. It is a qualifying (Q) subject: you must appear and pass, but its marks are not counted for percentage or division. The full scheme table is in the Complete Syllabus section above.

Which units carry the most weightage in AI & ML?
No marks-distribution table is given for this subject in the official syllabus, so unit weightages are not published. The unit-wise topic list and periods are given in the Complete Syllabus section above.

What practical work is there in AI & ML?
17 practicals/experiments, including: Load a Dataset and perform basic preprocessing.; Splitting of Dataset into Training and Testing Set.; Explore Python Libraries Pandas, NumPy, Matplotlib etc.. The complete list is in the Complete Syllabus section above.

Which books should I refer to for AI & ML?
Reference books are not listed for this subject in the official syllabus.

Is AI & ML a theory subject or a practical subject?
It is assessed as practicum (q) — see the examination scheme in the Complete Syllabus section above.

How can I download the AI & ML syllabus PDF?
The PDF is free to download — use the download link in the Download section below. It is prepared by KAMALDNP Academy from the official BTEUP syllabus.

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Download the AI & ML Syllabus PDF

Download the complete AI & ML syllabus PDF below — prepared by KAMALDNP Academy, with the full topic list, practicals and reference books.

Download the AI & ML Syllabus PDF

Source: BTEUP 3rd Year curriculum (folder2627), approved in C.D.C. meeting held on 16.07.2026. Topic lists follow the official syllabus; short explanations are the author's own plain-English summaries, not syllabus text. Please confirm with your own polytechnic for any local changes.