Schedule AIR.26
2 Months • 100 Hours
| Day |
10:30 – 13:00 (Lecture) |
13:00 – 14:30 (Lunch) |
14:30 – 17:00 (Hands-On) |
|---|---|---|---|
| Monday | Keynote Lecture & Robotics Lab Demonstration |
L U N C H |
Python Setup & Development Environment |
| Tuesday | Python Basics for AI | Python Programming (Module I) | |
| Wednesday | NumPy & Data Processing | Python Programming (Module II) | |
| Thursday | Mathematics for Machine Learning (Vectors & Matrices) | Linear Algebra Coding Practice | |
| Friday | Introduction to Machine Learning | ML Dataset Exploration | |
| Saturday | Supervised Learning Concepts | Regression Implementation | |
| Sunday | Classification Algorithms | Classification Implementation |
| Day |
10:30 – 13:00 (Lecture) |
13:00 – 14:30 (Lunch) |
14:30 – 17:00 (Hands-On) |
|---|---|---|---|
| Monday | Artificial Neural Network Fundamentals |
L U N C H |
ANN Implementation |
| Tuesday | Convolutional Neural Networks | CNN Coding Session | |
| Wednesday | Image Processing Basics | Object Detection Practice | |
| Thursday | Transfer Learning Concepts | Model Training Practice | |
| Friday | Natural Language Processing Basics | Text Processing Lab | |
| Saturday | Introduction to Robotics Systems | Robot Components Demonstration | |
| Sunday | Mobile Robot Architecture | Robot Simulation Demonstration |
| Day |
10:30 – 13:00 (Lecture) |
13:00 – 14:30 (Lunch) |
14:30 – 17:00 (Hands-On) |
|---|---|---|---|
| Monday | Introduction to ROS 2 Architecture |
L U N C H |
ROS Installation & Workspace Setup |
| Tuesday |
ROS Communication (Topics, Services, Actions) ROS for Mobile Robots & UAVs (Drones) |
ROS Node Programming | |
| Wednesday | Robot & Drone Simulation using Gazebo | Robot / Drone Simulation Practice | |
| Thursday | Mobile Robot & Drone Navigation Concepts | Navigation Practice (Ground Robot / Drone) | |
| Friday | Localization Techniques (AMCL, Sensor Fusion) | Localization Implementation | |
| Saturday | SLAM Fundamentals (2D Mapping) | Mapping Practice | |
| Sunday | Robot & Drone Vision Integration | Vision-Based Navigation Implementation |
| Day |
10:30 – 13:00 (Lecture) |
13:00 – 14:30 (Lunch) |
14:30 – 17:00 (Hands-On) |
|---|---|---|---|
| Monday | Robotics & Drone Hardware Overview |
L U N C H |
Robot & Drone Hardware Demonstration |
| Tuesday | Sensors & Actuators (Ground Robots & Drones) | Hardware Interfacing Practice | |
| Wednesday | Embedded Controllers & Communication Interfaces | Controller Programming Practice | |
| Thursday | Reinforcement Learning Fundamentals | RL-based Control Example (Robot / Drone) | |
| Friday | Robot & Drone System Integration | Mini Project Development | |
| Saturday | Project Development & Testing | Project Development & Testing | |
| Sunday | Project Evaluation & Certificate Distribution | Closing & Feedback Session |
| Day |
18:00 – 19:00 (Live Coding / Mentoring) |
Project Focus / Outcome |
|---|---|---|
| Monday | Problem Definition & Goal Setting | Identify a real-world ML problem (general, robotics, or drone-related), define objectives, scope, inputs, and expected outputs. |
| Tuesday | Data Collection | Collect datasets from public repositories, sensors, robotics logs, drone telemetry, APIs, or simulations. |
| Wednesday | Data Pre-processing | Data cleaning, handling missing values, normalization, feature engineering, and dataset preparation. |
| Thursday | Exploratory Data Analysis (EDA) | Visualize distributions, identify patterns and correlations, and extract insights using Python-based tools. |
| Friday | Model Selection | Select suitable ML models such as Linear Models, Tree-based methods, or Neural Networks based on data characteristics. |
| Saturday | Week Off | No Session |
| Sunday | Week Off | No Session |
| Day |
18:00 – 19:00 (Live Coding / Mentoring) |
Project Focus / Outcome |
|---|---|---|
| Monday | Model Training | Train machine learning or perception models using robotics or drone datasets (vision, navigation, control, or telemetry data). |
| Tuesday | Model Evaluation | Evaluate system performance using suitable metrics such as accuracy, precision, recall, latency, robustness, and real-time constraints. |
| Wednesday | Model Improvement | Improve model performance through hyperparameter tuning, architecture refinement, sensor fusion, or algorithm comparison. |
| Thursday | System Deployment | Integrate trained models into robotic systems, simulators, or drone control pipelines for testing and demonstration. |
| Friday | Online Evaluation & Certification | Project review and evaluation. Digital certificates issued; hard copies will be dispatched to registered addresses. |
| Saturday | Week Off | No Session |
| Sunday | Week Off | No Session |
| Day |
18:00 – 19:00 (Live Coding / Mentoring) |
Project Focus / Outcome |
|---|---|---|
| Monday | Feature Engineering | Design and optimize features for robotics and drone data, including sensor fusion, temporal features, and spatial representations. |
| Tuesday | Advanced Models | Explore advanced models such as ensemble learning, deep neural networks, and hybrid ML approaches for autonomous systems. |
| Wednesday | Cross-Validation & Robustness | Apply cross-validation and robustness testing to ensure model reliability under real-world robotic and drone conditions. |
| Thursday | Model Optimization | Perform hyperparameter tuning and optimization using Grid Search, Random Search, or Bayesian optimization techniques. |
| Friday | Project Review & Refinement | Students present project progress and results. Expert feedback provided for performance improvement and final integration. |
| Saturday | Week Off | No Session |
| Sunday | Week Off | No Session |
| Day |
18:00 – 19:00 (Live Coding / Mentoring) |
Project Focus / Outcome |
|---|---|---|
| Monday | ML–Robotics & Drone Integration | Integrate trained machine learning models with robotic or drone platforms for perception, navigation, control, or decision-making tasks. |
| Tuesday | Simulation & Controlled Testing | Test integrated robotic and drone systems in simulation environments (e.g., Gazebo / simulators) and analyze system behavior. |
| Wednesday | Performance & Safety Analysis | Evaluate accuracy, latency, robustness, and safety of autonomous systems under different operating conditions. |
| Thursday | Final Project Completion | Finalize implementation, documentation, project report, and presentation material for final evaluation. |
| Friday | Final Presentation & Evaluation | Students present complete robotics or drone-based projects. Evaluation based on design, implementation quality, innovation, and technical understanding. |
| Saturday | Week Off | No Session |
| Sunday | Week Off | No Session |