Our Robotics and Artificial Intelligence course syllabus for 2025 provides a detailed robotics course outline along with advanced AI modules. You’ll learn robotics programming, AI algorithms, automation systems, and real-world applications. Download the complete syllabus in PDF format and explore a curriculum designed for industry readiness and innovation.
Robotics and Artificial Intelligence Course Syllabus
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Syllabus for The Robotics and Artificial Intelligence Syllabus Course
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Module 1: Introduction to Robotics
1
- 1.1 History and Evolution of Robotics:
- Milestones in robotics development.
- Laws of Robotics (Asimov).
- Ethical considerations in robotics design and deployment.
- 1.2 Robot Systems:
- Components of a robot: sensors, actuators, effectors, control systems.
- Classification of robots: industrial robots, mobile robots, service robots, humanoids.
- Robot kinematics and dynamics: forward and inverse kinematics, motion planning.
- 1.3 Robotics Platforms:
- Introduction to robotics platforms (e.g., Arduino, Raspberry Pi, ROS).
- Basic programming and interfacing with sensors and actuators.
- Hands-on experience with robotic kits (e.g., line-following robots, robotic arms).
Module 2: Fundamentals of Artificial Intelligence
2
- 2.1 Introduction to AI:
- Definitions and goals of AI.
- Different approaches to AI: symbolic AI, machine learning, deep learning.
- AI applications in everyday life.
- 2.2 Search Algorithms:
- Uninformed search: breadth-first search, depth-first search.
- Informed search: A*, greedy best-first search.
- Constraint satisfaction problems.
- 2.3 Knowledge Representation and Reasoning:
- Propositional and first-order logic.
- Rule-based systems and expert systems.
- Knowledge representation using ontologies.
Module 3: Machine Learning
3
- 3.1 Supervised Learning:
- Linear regression, logistic regression, support vector machines.
- Decision trees, random forests.
- Overfitting and underfitting, model evaluation metrics.
- 3.2 Unsupervised Learning:
- Clustering algorithms (k-means, hierarchical clustering).
- Dimensionality reduction (PCA).
- 3.3 Reinforcement Learning:
- Markov Decision Processes (MDPs).
- Q-learning, deep Q-networks (DQN).
- Applications in robotics (e.g., robot control, path planning).
Module 4: Deep Learning
4
- 4.1 Neural Networks:
- Perceptrons, multi-layer perceptrons.
- Backpropagation and gradient descent.
- 4.2 Convolutional Neural Networks (CNNs):
- Image recognition, object detection.
- 4.3 Recurrent Neural Networks (RNNs):
- Natural Language Processing (NLP), time series analysis.
Module 5: Robotics and AI Applications
5
- 5.1 Autonomous Vehicles:
- Sensor fusion, path planning, motion control.
- Computer vision for self-driving cars.
- 5.2 Robotics in Healthcare:
- Surgical robotics, rehabilitation robots, assistive technologies.
- 5.3 Industrial Robotics:
- Manufacturing automation, assembly lines, robotics in logistics.
- 5.4 AI in Healthcare:
- Medical image analysis, disease prediction, drug discovery.
- 5.5 AI in Natural Language Processing:
- Chatbots, machine translation, sentiment analysis.
Module 6: Ethical and Societal Considerations
6
- 6.1 AI Ethics:
- Bias and fairness in AI systems.
- Privacy and security concerns.
- Job displacement and the future of work.
- The impact of AI on human values and society.
- 6.2 Responsible AI Development:
- Explainable AI (XAI).
- AI safety and robustness.
- The role of humans in the AI development process.
Breakdown of Robotics and Artificial Intelligence Syllabus Course Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
June 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
Course Fee
June 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
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