Masters in AI & Robotics

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Course Description :

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition,Learning,Planning, Problem solving. Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Top Skills You Will Learn

  • Introduction to Artificial Intelligence
  • Introduction to Robotics
  • Introduction to Computer Vision
  • Introduction to Neural Networks
  • Working with CNNs
  • Introduction to EdgeAI & Robotics
  • Introduction to ROS
  • Application Development for EdgeAI with ROS

Who Is This Program For?

Engineers, Software and IT Professionals, Data Professionals

Job Opportunities

Machine Learning Engineer, Data Scientist, AI Architect, Business Analyst, Product Analyst

Minimum Eligibility

Bachelor’s Degree with minimum 1 year of work experience or a degree in Mathematics or Statistics

Module 1 : Introduction to Artificial Intelligence

  • What is Artificial Intelligence?
  • Hype vs. Reality
  • Current Industrial Applications using AI at its core
  • The way forward with Artificial Intelligence
  • Industry Verticals and future AI
  • What to learn for Artificial Intelligence?
  • Example Case Studies

Module 2 : Introduction to Robotics

  • What is Robotics?
  • Current Industrial Applications using AI
  • Challenges for developing AI for robotics applications
  • Way forward in Robotics & AI
  • Example Case Study

Module 3 : Introduction to Computer Vision

  • What is Image?
  • What is Image Processing & its techniques?
  • Current trends in Image Processing
  • Introduction to OpenCV
  • Hands on Experience with OpenCV & Image Processing

– Filtering Techniques – Linearity and Convolution – Edge Detection and Gradients – Hough Transform – Convolution – Perspective Imaging – Extrinsic & Intrinsic Parameters – Camera Calibration – Introduction to features – Motion & Object Tracking – The Kalman Filter – Bayes Filter – Particle Filter – Recognition & Classification – Support Vector Machine

  • Mini Project – OpenCV + Image Processing

Module 4 : Introduction to Neural Networks

  • Neural Network based techniques
  • Working with Neural Networks
    • Data as a core component
    • Model Training
    • Validation
    • NN Performance Parameters
  • Introduction to popular CNN architectures
  • Introduction to CNN Frameworks
    • Need of a CNN framework
    • Most Popular CNN frameworks
      • Tensorflow & Keras
      • PyTorch
    • Introduction to PyTorch
    • Hands on Experience with Training CNN with PyTorch

Module 5 : Working with CNNs

  • Image Recognition and Classification Techniques
  • Data Annotations & Labeling
  • Mini – Project – Working on CNN model training with readily available dataset for classification problem with PyTorch based CNN Model

Module 6 : Introduction to EdgeAI & Robotics

  • Introduction to EdgeAI
  • Difference between Heavy Computing AI & need of lightweight computing in AI
  • Introduction to Intel OpenVINO platform
  • Mini-Project – Hands on experience working with RaspberryPI & Intel Movidius Neural Stick & running Object Detection Model with OpenVINO

Module 7 : Introduction to ROS

  • Introduction to Robotic Operating System
  • ROS Architecture
  • The Pub/Sub way of data transfers in ROS
  • ROS APIs

Module 8 : Application Development for Edge AI with ROS

  • Extension of Mini-Project to the major with application development on RPI, Intel Neural Stick with Intel OpenVINO