Module 1 – Introduction to Robotics & Machine Learning

• Introduction to Robotics
• Introduction to Machine Learning
• Evolution of Intelligent Robotics
• Types of Robots
• Applications of Robotics and ML
• Components of Robotic Systems
• Role of ML in Robotics
• Intelligent Automation Concepts

Module 2 – Electronics Fundamentals

• Basic Electrical Concepts
• Voltage, Current and Resistance
• Ohm's Law
• AC and DC
• Electronic Components
• Resistors, Capacitors and Diodes
• LEDs and Switches
• Breadboard
• Basic Circuit Building

Module 3 – Microcontrollers & Embedded Systems

• Introduction to Microcontrollers
• Arduino Fundamentals
• Arduino Board Components
• Digital and Analog Pins
• Input and Output
• Arduino IDE
• Program Uploading
• Serial Monitor
• Embedded System Applications

Module 4 – Programming for Robotics

• Programming Fundamentals
• C/C++ Basics
• Variables and Data Types
• Operators
• Conditional Statements
• Loops
• Functions
• Arrays
• Basic Robotics Programming

Module 5 – Sensors in Robotics

• Introduction to Sensors
• IR Sensors
• Ultrasonic Sensors
• LDR Sensors
• Temperature Sensors
• Humidity Sensors
• Motion Sensors
• Proximity Sensors
• Sensor Interfacing
• Sensor Data Collection

Module 6 – Motors & Actuators

• Introduction to Motors
• DC Motors
• Servo Motors
• Stepper Motors
• Motor Drivers
• PWM Control
• Speed and Direction Control
• Actuators
• Motor Interfacing

Module 7 – Robot Assembly & Basic Control

• Robot Chassis
• Wheels and Gears
• Motor Assembly
• Battery and Power Supply
• Controller Integration
• Sensor Integration
• Robot Wiring
• Basic Movement Control
• Robot Testing and Debugging

Module 8 – Basic Robotic Applications

• Line Following Robot
• Obstacle Avoiding Robot
• Bluetooth-Controlled Robot
• Remote-Controlled Robot
• Sensor-Based Navigation
• Motor Control
• Robot Movement Algorithms
• Practical Troubleshooting

Module 9 – Python for Robotics & ML

• Introduction to Python
• Variables and Data Types
• Conditional Statements
• Loops
• Functions
• Lists, Tuples and Dictionaries
• Modules and Libraries
• NumPy Basics
• Python Applications in Robotics

Module 10 – Machine Learning Fundamentals

• Introduction to Machine Learning
• AI vs ML
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Training and Testing Data
• Features and Labels
• Model Training
• Prediction and Classification

Module 11 – Data Processing for Robotics

• Introduction to Data
• Collecting Sensor Data
• Data Cleaning
• Missing Data
• Data Transformation
• Feature Selection
• Data Normalization
• Training Dataset Preparation
• Sensor Data Analysis

Module 12 – Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• K-Nearest Neighbors
• Support Vector Machines
• Clustering
• Classification Algorithms
• Regression Applications

Module 13 – Machine Learning with Python

• Introduction to Scikit-learn
• Dataset Loading
• Data Preprocessing
• Model Training
• Model Prediction
• Model Evaluation
• Accuracy and Error Metrics
• Model Comparison
• Saving and Loading Models

Module 14 – Computer Vision for Robotics

• Introduction to Computer Vision
• Digital Images
• Image Processing Basics
• OpenCV Fundamentals
• Image Reading and Processing
• Color Detection
• Shape Detection
• Object Detection Concepts
• Camera-Based Robotics

Module 15 – ML-Based Robot Control

• Intelligent Robot Decision Making
• Sensor-Based Prediction
• ML-Based Navigation
• Object Classification
• Obstacle Detection
• Intelligent Movement
• Adaptive Robot Behaviour
• Machine Learning-Based Control Systems

Module 16 – Advanced Intelligent Robotics

• Autonomous Robots
• Intelligent Navigation
• Object Recognition
• Predictive Maintenance Concepts
• Robot Behaviour Learning
• Human-Robot Interaction
• Smart Manufacturing
• ML Applications in Industrial Robotics

Module 17 – Robotics Data & Model Optimization

• Feature Engineering
• Model Accuracy Improvement
• Training and Validation
• Overfitting and Underfitting
• Model Optimization
• Hyperparameter Concepts
• Performance Evaluation
• Real-Time Sensor Data Processing

Module 18 – Advanced Robotics & ML Integration

• Integrating ML Models with Robots
• Python-Based Robot Control
• Microcontroller and Computer Communication
• Real-Time Decision Making
• Camera and Sensor Integration
• Edge AI Concepts
• Intelligent Automation
• Robotics Simulation Concepts

Module 19 – Practical Robotics with ML Projects

Students will work on practical projects such as:

• ML-Based Object Classification Robot
• Intelligent Obstacle Detection Robot
• Smart Line Following Robot
• Camera-Based Object Detection Robot
• Gesture-Controlled Robot
• ML-Based Sorting System
• Predictive Maintenance Prototype
• Smart Surveillance Robot
• Intelligent Robotic Vehicle

Module 20 – Final Robotics & ML Project

• Project Planning
• Problem Definition
• Hardware Selection
• Sensor Data Collection
• Dataset Preparation
• ML Model Development
• Robot Integration
• Testing and Debugging
• Model Optimization
• Project Documentation
• Final Project Demonstration