Course Objective
Prepare students to use the integration of Embedded Systems and Artificial Intelligence to resolve challenges in industrial systems. Students will learn how incorporating machine learning and deep learning algorithms with embedded systems can streamline processes and automate tasks. Focus will be on developing system approach through integrated projects to master specific methods and tools applied in the aeronautics, space, automobiles, and multimedia domains. The course will impart an in-depth knowledge focussing on both theoretical and practical aspects.
Fee Structure
The course comprises of 6 modules mentioned under the Course Syllabus below. Student can enrol for the full course (6 modules) or is free to enrol for individual modules.
Registration Fee: Rs.500/-
Full Course Fees: Rs.94500/-
Each Module Course Fees: Rs.17500/-
Registration fees and course fees
should to be paid online or by Demand Draft.
Online Payment Details: Account Name – ICIT PVT LTD
Account Number: 27205000514
IFSC Code: SCBL0036107
Bank: Standard Chartered Bank
Bank Branch: Aundh Branch, 163, Harsh Orchid, New DP Road, Nagras RoadMall, Ward no 8, Aundh, Pune 411007
Demand Draft: To be drawn on any nationalized bank in favour of “ICIT Pvt. Ltd., Pune” and should
be payable at par.
Note that registration and course fees once paid are non refundable.
Project Overview
- Objective: To apply data analytics and AI techniques to a real-world problem or dataset, demonstrating the ability to gather, analyze, interpret, and present data.
- Scope: Projects can be individual or group-based, focusing on industries like finance, healthcare, retail, or any sector relevant to the students’ interests or local economic needs.
Stages of the Project
Stage 1: Project Proposal
- Deliverables:
- Project title and team members (if applicable)
- A clear statement of the problem
- Objectives and expected outcomes
- Preliminary research and existing solutions
Stage 2: Data Collection and Preparation
- Deliverables:
- Description of data sources
- Data collection methodology
- Data cleaning and preprocessing steps
Stage 3: Exploratory Data Analysis
- Deliverables:
- Statistical summaries and visualizations of the data
- Initial insights and hypotheses based on the data
Stage 4: Model Development
- Deliverables:
- Selection of appropriate AI and machine learning models
- Training models and tuning parameters
- Validation and testing of models
Stage 5: Results and Interpretation
- Deliverables:
- Detailed analysis of model results
- Comparison with initial hypotheses and objectives
- Discussion of the model’s effectiveness and limitations
Stage 6: Final Presentation and Report
- Deliverables:
- Comprehensive report documenting all stages of the project
- Presentation of the project outcomes to an audience which may include peers, faculty, and industry professionals
This structured approach ensures thorough exploration and application of data analytics and AI techniques throughout the project lifecycle.
