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<!DOCTYPE html>
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<header class="section-header">
<h3>AI61006: Artificial Intelligence for Cyber Physical Systems</h3>
</header>
<p>
This course on AI for Cyber-Physical Systems (CPS) introduces the foundational concepts of CPS, where physical processes like machines, vehicles, or devices are tightly integrated with computation and control. With a focus on sensing, planning, and acting, the course teaches students how to view real-world, domain-specific problems through the lens of CPS and solve them using AI techniques. By combining theoretical understanding with practical insights, students learn how to design intelligent systems that can perceive their environment, make decisions, and interact with the physical world to develop skills that are essential for applications like autonomous vehicles, smart infrastructure, and industrial automation.
</p>
<h1>Objective</h1>
<ul>
<li>How to formulate your domain specific problem as a CPS</li>
<li>How to apply some suitable AI techniques to solve a CPS problem</li>
</ul>
<h1>Modus Operandi</h1>
<ul>
<li>Ask doubts: 5%</li>
<li>Five minutes teaching (Pick any small topic that is taught in the class before your schedule and teach it for five minutes.): 10%</li>
<li>Attendance: 5%</li>
<li>Course project: 10%</li>
<ul>
<!-- <li>Refer to <a href="#student_projects">course-project</a> details.</li> -->
</ul>
<li>Mid-sem: 30%</li>
<li>End-sem: 40%</li>
</ul>
<h1>Syllabus</h1>
<p> The course is dynamic and the content may include explanations of the questions asked by students as well as some LinkedIn or X discussions related to AI. The following syllabus will be followed to give story to mathematics and programming.</p>
<p> In other words, more focus will be given to fundamentals and underlying rigorous mathematics instead of easy applications of CPS. </p>
<h1>Module 1: Introduction</h1>
<ul>
<li>What is AI?</lil>
<li>What is CPS?</lil>
<li>Key features of CPS</lil>
<li>Modeling, sensing, planning and acting for a physical system</li>
</ul>
<h1>Module 2: Acting</h1>
<ul>
<li>Case study</lil>
<li>Reinforcement learning in Gymnasium</lil>
<li>Deep Reinforcement Learning in HuggingFace</lil>
<li>MPC (if time permits)</lil>
</ul>
<h1>Module 3: Planning</h1>
<ul>
<li>Case study</lil>
<li> \( A^\ast \)</lil>
<li>RRT</lil>
<li>PRM</lil>
</ul>
<h1>Module 4: Sensing</h1>
<ul>
<li>Case study</lil>
<li>Overview of sensors</li>
<li>Kalman filter and sensor fusion</li>
<li>CNN</lil>
<li>YOLO</lil>
</ul>
<h1>Books and references</h1>
<p>Slides with topicwise references will be provided. A single book with all topics does not exist.</p>
</div>
</section>
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The course content is comprehensive, covering all essential topics relevant to the subject matter. The structure of
the course is well-organized, with clear objectives and logical progression from basic to advanced concepts. The
inclusion of practical examples and real-world applications enhances understanding and relevance for students.
Regular assessments and feedback mechanisms facilitate continuous learning and improvement.
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Can't stress about how helpful sir is. He concentrates on everyone in the class. Teaches really well. And also helps
anyone who is struggling by sending supporting resources. Truly felt like an IITian after this course. He makes it
intellectually stimulating by giving real life examples and also make us think to solve real life problems.
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<p>We are focused on research at the intersection of control theory and Artificial Intelligence (AI). The real world applications of AI require safety guarantee and explainability of the AI module. In addition, several modules such as sensing and perception, planning and action need to interact to complete the feedback loop. Our main focus is in planning and decision making but we also consider sensing and actuation along with the effects of communication channels whenever required.
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CET 021, Takshashila building <br>
IIT Kharagpur, Kharagpur, 721302<br>
India <br>
<strong>Phone:</strong> +91 3222-214-530<br>
<strong>Email:</strong> pkmishra@ai.iitkgp.ac.in<br>
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