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B.E Artificial Intelligence and Machine Learning Reinforcement Learning Syllabus - Mumbai University 2026

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

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Semester 8 · Fourth Year AI-ML

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Syllabus for Reinforcement Learning

Semester 8 · Fourth Year AI-ML

Module 1: Introduction and Fundamentals of Robotics and Automation

  • Automation and its types, definition of Robotics and a Robot, History of Robotics, Advantages and Disadvantages of Robot, Robotic Manipulators, Robot Motions, Robot Anatomy, Links and Joints, Classification of Robots, Specification of Robot, Applications of Robots

Module 2: Direct and Inverse Kinematics

  • Direct (Forward) Kinematics: Homogeneous coordinates, Link coordinates, Coordinate frame, coordinate transform, Arm equations, An example – Four Axis SCARA. Inverse Kinematics: Inverse kinematics problem, Tool Configuration, An example – Four Axis SCARA.

Module 3: Actuators and Sensors

  • Characteristics of Actuating Systems, Comparison of Actuating Systems, Hydraulic Devices, Pneumatic Devices, Electric Motors, Magneto strictive Actuators Sensor Characteristics, Position Sensors, Velocity Sensors, Acceleration Sensors, Force and Pressure Sensors, Torque Sensors, Light and Infrared Sensors, Touch and Tactile Sensors, Proximity Sensors, Sniff Sensors, Vision Systems, Voice Synthesizer

Module 4: Motions, velocities and dynamic analysis of force

  • Differential relationship, Jacobian, Differential motions of a frame and robot, Inverse Jacobian, Lagrangian mechanics, Moments of Inertia, Dynamic equations of robots, Transformation of forces and moment between coordinate frames

Module 5: Self-configuring Robots and AI integration

  • Historical perspective of AI in Robotics, Uncertainty in Robotics Reinforcement Learning: Basic overview, examples, elements, Tabular Solution Methods - Multiarmed bandits, Finite Markov decision process, Dynamic programming (Policy Evaluation, Policy Iteration, Value Iteration), Monte Carlo Methods.

Module 6: Applications of Robotics for Automation

  • Robot Application in Manufacturing: Material Transfer - Material handling, loading and unloading Processing - spot and continuous arc welding & spray painting – Assembly Inspection, Selected Embedded System based Applications: Database Applications (smart cards), Process-Control (Fuzzy logic), Robot application in Medical, Industrial Automation, Security

Useful Links

  • 1 Robert Shilling, “Fundamentals of Robotics-Analysis and control”, PHI, 2003.
  • 2 Saeed B. Niku, “Introduction to Robotics Analysis, Systems, Applications”,3rd Edition, Wiley, 2019.
  • 3 Saha, S.K., “Introduction to Robotics”, 2nd Edition, McGraw-Hill Higher Education, New Delhi, 2014.
  • 4 Staughard, Robotics and AI, Prentice Hall of India
  • 5 Ashitava Ghoshal, “Robotics-Fundamental Concepts and Analysis”, Oxford University Press, Sixth impression, 2010
  • 6 Mukherjee S., “Robotics Process Automation”, 1st Edition, Khanna Publishing House, New Delhi, 2020.
  • 1 John J. Craig, “Introduction to Robotics – Mechanics & Control”, 3rd Edition, Pearson Education, India, 2009
  • 2 Mark W. Spong & M. Vidyasagar, “Robot Dynamics & Control”, 2nd Wiley India Pvt. Ltd., 2004
  • 3 Principles of Robot Motion – Theory, Algorithms and Implementation by Howie Choset, Lynch, PHI.
  • 1 https://swayam.gov.in/nc_details/NPTEL
  • 2 https://www.udemy.com/course/robotics-course/
  • 3 https://www.coursera.org/courses?query=robotics

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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