Prerequisite Courses, if any: Engineering Mathematics
(Linear Algebra, Probability & Statistics, Calculus), Digital signal processing, Data Structures and
Algorithms
Companion Course, if any:
Course Objectives:
To make students understand
1. To introduce the fundamentals of Artificial Intelligence, Machine Learning, and data-driven systems.
2. To develop students’ ability to preprocess, analyze, and visualize engineering datasets.
3. To enable students to implement Machine Learning algorithms using Python-based tools.
4. Introduce emerging technologies such as Edge AI, TinyML, AIoT, and Generative AI.
5. Prepare students for industry-oriented AI projects, internships, research, and multidisciplinary applications.
Course Outcomes:
After successful completion of the course, students will be able to:
CO1: Explain Machine Learning fundamentals, workflow and AI applications in Electronics and Telecommunication Engineering.
CO2: Apply data preprocessing, visualization, and exploratory analysis techniques using Python libraries.
CO3: Implement Supervised Machine Learning Algorithms for predication and classification problems.
CO4: Apply Unsupervised learning techniques and Analyze engineering datasets using clustering approaches.
CO5: Develop AI base mini-projects using modern tools, real-world datasets and emerging AI technologies.
- Teacher: Ujwala Darekar