AI (Robotics) Course Outline

Course Contents / Lesson Plan

Course Title: AI (Robotics)

Duration: 3 Months

 

MODULES

Schedu

led Weeks

 Module Title Days Hours Learning Units Home Assignment
Week 1 Introduction to AI and Robotics Day 1 Hour 1 Course Introduction and Expectations  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

· Task 1

 

Details may be seen at Annexure-I
Hour 2 Intro to AI and Robotics
Hour 3 Job Market Overview
Hour 4 Work Ethics in Institute
Day 2 Hour 1 History of AI and Robotics
Hour 2 Current State of AI and Robotics
Hour 3 Applications of AI and Robotics
Hour 4 Ethical Considerations in AI and Robotics
Day 3 Hour 1 Introduction to Programming Languages used in AI and Robotics
Hour 2 Variables, Data Types, and Operators
Hour 3 Control Structures and Functions
Hour 4 Hands-on Practice with a Programming Language
Day 4 Hour 1 Introduction to Machine Learning (ML)
Hour 2 Supervised vs. Unsupervised Learning

 

Hour 3 Linear Regression
Hour 4 Hands-on Practice with a ML Algorithm
Day 5 Hour 1 Introduction to Computer Vision
Hour 2 Image Processing Techniques
Hour 3 Feature Extraction
Hour 4 Hands-on Practice with a Computer Vision Algorithm
Week 2 Programming Fundamentals Day 1 Hour 1 Success Stories of AI and Robotics  

 

 

 

 

 

 

 

 

 

 

 

· Task 2

 

Details may be seen at Annexure-I
Hour 2 Recap of Programming Concepts
Hour 3 Introduction to Object- Oriented Programming (OOP)
Hour 4 Hands-on Practice with OOP
Day 2 Hour 1 Data Structures and Algorithms
Hour 2 Recursion
Hour 3 Big O Notation
Hour 4 Hands-on Practice with Data Structures and Algorithms
Day 3 Hour 1 Introduction to Version Control Systems (VCS)

 

Hour 2 Git Basics
Hour 3 Branching and Merging
Hour 4 Hands-on Practice with Git
Day 4 Hour 1 Introduction to Web Development
Hour 2 HTML, CSS, and

JavaScript
Hour 3 Web Frameworks
Hour 4 Hands-on Practice with Web Development
Day 5 Hour 1 Introduction to Cloud Computing
Hour 2 Cloud Providers
Hour 3 Infrastructure as Code
Hour 4 Hands-on Practice with Cloud Computing
Week 3 Machine Learning Basics Day 1 Hour 1 Motivational Lecture on AI and Robotics  

 

 

 

· Task 3

 

Details may be seen at Annexure-I
Hour 2 Multivariate Linear Regression
Hour 3 Logistic Regression
Hour 4 Hands-on Practice with Regression Algorithms
Day 2 Hour 1 Support Vector Machines (SVM)

 

Hour 2 Kernel Tricks
Hour 3 Hands-on Practice with SVM
Hour 4 Hands-on Practice with SVM
Day 3 Hour 1 Decision Trees and Random Forests
Hour 2 Ensemble Methods
Hour 3 Hands-on Practice with Decision Trees and Random Forests
Hour 4 Decision Trees and Random Forests
Day 4 Hour 1 Introduction to Neural Networks
Hour 2 Perceptron
Hour 3 Multi-Layer Perceptron (MLP)
Hour 4 Hands-on Practice with Neural Networks
Day 5 Hour 1 Introduction to Deep Learning
Hour 2 Convolutional Neural Networks (CNNs)
Hour 3 Recurrent Neural Networks (RNNs)
Hour 4 Hands-on Practice with CNNs and RNNs

 

Week 4 Computer Vision Day 1 Hour 1 Success Stories of AI and Robotics  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

· Task 4

Details may be seen at Annexure-I
Hour 2 Introduction to Image Processing
Hour 3 Image Filtering
Hour 4 Hands-on Practice with Image Filtering
Day 2 Hour 1 Edge Detection
Hour 2 Feature Extraction Techniques
Hour 3 Hands-on Practice with Feature Extraction
Hour 4 Hands-on Practice with Feature Extraction
Day 3 Hour 1 Object Recognition
Hour 2 Object Tracking
Hour 3 Hands-on Practice with Object Recognition and Tracking
Hour 4 Hands-on Practice with Object Recognition and Tracking
Day 4 Hour 1 Semantic Segmentation
Hour 2 Instance Segmentation
Hour 3 Hands-on Practice with Segmentation

 

Hour 4 Hands-on Practice with Segmentation
Day 5 Hour 1 Introduction to 3D Computer Vision
Hour 2 Stereo Vision
Hour 3 Depth Estimation
Hour 4 Hands-on Practice with 3D Computer Vision
Week 5 Natural Language Processing Day 1 Hour 1 Introduction to Natural Language Processing (NLP)  

 

 

 

 

 

 

 

 

 

 

 

 

· Task 5

Details may be seen at Annexure-I
Hour 2 Text preprocessing and cleaning
Hour 3 Tokenization and stemming
Hour 4 Part-of-speech tagging
Day 2 Hour 1 Named Entity Recognition (NER)
Hour 2 Chunking and parsing
Hour 3 Word embeddings
Hour 4 Language modeling
Day 3 Hour 1 Sentiment analysis
Hour 2 Topic modeling

 

Hour 3 Text classification
Hour 4 Information retrieval
Day 4 Hour 1 Machine translation
Hour 2 Dialogue systems
Hour 3 Text summarization
Hour 4 Natural Language Generation (NLG)
Day 5 Hour 1 Ethical considerations in NLP
Hour 2 Emerging trends in NLP
Hour 3 Practical applications of NLP
Hour 4 Hands-on NLP project
Week 6 Reinforcement Learning Day 1 Hour 1 Introduction to Reinforcement Learning (RL)  

 

 

 

·  Task 6

 

Details may be seen at Annexure-I
Hour 2 Markov Decision Processes (MDPs)
Hour 3 Value iteration and policy iteration
Hour 4 Monte Carlo methods
Day 2 Hour 1 Temporal Difference (TD) learning

 

Hour 2 SARSA algorithm
Hour 3 Q-Learning
Hour 4 Deep Q-Learning
Day 3 Hour 1 Exploration vs exploitation trade-off
Hour 2 Multi-armed bandits
Hour 3 Upper Confidence Bound (UCB) algorithm
Hour 4 Thompson Sampling
Day 4 Hour 1 Policy Gradient Methods
Hour 2 REINFORCE algorithm
Hour 3 Actor-Critic methods
Hour 4 Asynchronous Advantage Actor-Critic (A3C) algorithm
Day 5 Hour 1 Multi-Agent RL
Hour 2 Cooperative and competitive scenarios
Hour 3 Multi-Agent Deep RL
Hour 4 Applications of RL in robotics
Week 7 Deep Learning Basics Day 1 Hour 1 Introduction to Deep Learning (DL) ·  Task 7

 

Hour 2 Artificial Neural Networks (ANNs) Details may be seen at Annexure-I
Hour 3 Activation functions
Hour 4 Forward and backward propagation
Day 2 Hour 1 Convolutional Neural Networks (CNNs)
Hour 2 Pooling layers
Hour 3 Convolutional layers
Hour 4 Batch Normalization
Day 3 Hour 1 Recurrent Neural Networks (RNNs)
Hour 2 Long Short-Term Memory (LSTM) networks
Hour 3 Gated Recurrent Units (GRUs)
Hour 4 Word-level language modeling
Day 4 Hour 1 Autoencoders
Hour 2 Variational Autoencoders (VAEs)
Hour 3 Generative Adversarial Networks (GANs)
Hour 4 Deep Reinforcement Learning (DRL) with DL
Day 5 Hour 1 Transfer Learning

 

Hour 2 Fine-tuning and feature extraction
Hour 3 Domain adaptation
Hour 4 Practical applications of DL
Week 8 Robotics Control Day 1 Hour 1 Introduction to Robotics Control  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

·  Task 8

Details may be seen at Annexure-I
Hour 2 Degrees of freedom and joint types
Hour 3 Forward kinematics
Hour 4 Inverse kinematics
Day 2 Hour 1 Differential kinematics
Hour 2 Jacobians and manipulability
Hour 3 Control of robot arms
Hour 4 Inverse dynamics
Day 3 Hour 1 Robot dynamics
Hour 2 Lagrangian dynamics
Hour 3 Newton-Euler equations
Hour 4 Robust control of robots
Day 4 Hour 1 Trajectory planning and control

 

Hour 2 Motion planning algorithms
Hour 3 Path following control
Hour 4 Feedback linearization
Day 5 Hour 1 Practical applications of robotics control
Hour 2 Emerging trends in robotics control
Hour 3 Robotics control
Hour 4 Revision of complete topic
Week 9 Reinforcement Learning for Robotics Day 1 Hour 1 Introduction to Reinforcement Learning for Robotics  

 

 

 

 

 

 

 

 

 

·  Task 9

Details may be seen at Annexure-I
Hour 2 Robotics Applications of RL
Hour 3 Markov Decision Processes (MDPs)
Hour 4 Markov Decision Processes (MDPs)
Day 2 Hour 1 Q-Learning
Hour 2 Deep Q-Learning
Hour 3 Experience Replay
Hour 4 Discussion session

 

Day 3 Hour 1 Policy Gradient Methods
Hour 2 Actor-Critic Methods
Hour 3 Proximal Policy Optimization (PPO)
Hour 4 Proximal Policy Optimization (PPO)
Day 4 Hour 1 Multi-Agent RL
Hour 2 Decentralized and Centralized RL
Hour 3 Cooperative and Competitive RL
Hour 4 Discussion
Day 5 Hour 1 RL for Robotics Case Studies
Hour 2 Industrial Automation
Hour 3 Industrial Automation
Hour 4 Autonomous Driving
Week 10 Advanced Computer Vision Day 1 Hour 1 Introduction to Advanced Computer Vision · Task 10

Details may be seen at Annexure-I
Hour 2 Object Detection
Hour 3 Object Tracking
Hour 4 Discussion

 

Day 2 Hour 1 Semantic Segmentation
Hour 2 Instance Segmentation
Hour 3 Mask R-CNN
Hour 4 Discussion
Day 3 Hour 1 Generative Models
Hour 2 Variational Autoencoders
Hour 3 Generative Adversarial Networks (GANs)
Hour 4 Discussion
Day 4 Hour 1 Video Understanding
Hour 2 Optical Flow
Hour 3 Action Recognition
Hour 4 Action Recognition
Day 5 Hour 1 3D Computer Vision
Hour 2 Monocular Depth Estimation
Hour 3 RGB-D Reconstruction
Hour 4 Complete topic revision

 

Week 11 Deep Reinforcement Learning Day 1 Hour 1 Introduction to Deep Reinforcement Learning (DRL)  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

·  Task 11 Details may be seen at Annexure-I
Hour 2 DRL Frameworks
Hour 3 DRL Frameworks
Hour 4 DQN Revisited
Day 2 Hour 1 Deep Policy Gradient Methods
Hour 2 REINFORCE
Hour 3 Actor-Critic Methods
Hour 4 Discussion
Day 3 Hour 1 Asynchronous RL
Hour 2 Asynchronous RL
Hour 3 A3C
Hour 4 Distributed RL
Day 4 Hour 1 Exploration Strategies
Hour 2 Epsilon Greedy
Hour 3 Boltzmann Exploration

 

Hour 4 Discussion
Day 5 Hour 1 RL for Games
Hour 2 Atari Games
Hour 3 AlphaGo and AlphaZero
Hour 4 Complete topic revision
Week 12 Robotics Perception Day 1 Hour 1 Introduction to Robotics Perception · Task 12

Details may be seen at Annexure-I

 

Final Project
Hour 2 Sensors in Robotics
Hour 3 Sensors in Robotics
Hour 4 Cameras
Day 2 Hour 1 Depth Perception
Hour 2 Stereo Vision
Hour 3 Time of Flight (ToF)
Hour 4 Time of Flight (ToF)
Day 3 Hour 1 LiDAR
Hour 2 Types of LiDAR
Hour 3 Point Cloud Processing

 

Hour 4 Point Cloud Processing
Day 4 Hour 1 Simultaneous Localization and Mapping (SLAM)
Hour 2 Types of SLAM
Hour 3 Visual SLAM
Hour 4 Discussion
Day 5 Hour 1 Robotics Perception Case Studies
Hour 2 Self-Driving Cars
Hour 3 Autonomous Drones
Hour 4 Applications in daily life

 

Tasks for Certificate in AI (Robotics)

 

Task No. Task Description Week
1. Simple robot Build a simple robot using a kit Week 1
2. Basic coding Write a program to control the robot built in Week 1 Week 2
3. Machine Learning implementation Implement a simple ML model to make the robot move based on data from its sensors Week 3
4. Computer Vision Implementation Build a program to detect and track objects using a camera Week 4
5. Chatbot Build a chatbot that can answer simple questions Week 5
6. Robot (RL technique) Building a robot that can navigate through a maze using RL techniques Week 6
7. Implement DL model Implement a simple DL model to recognize objects in images Week 7
8. Robotic Arm Build a program to control a robot arm Week 8
9. Building a robot that can learn to perform tasks through RL Build a robot that can learn to perform tasks through RL Week 9
10. Computer Vision Build a program to detect and track objects in real-

time using a camera

 Week10
11. DRL Techniques Build a robot that can learn to perform complex tasks using DRL techniques Week11
12. LiDAR and SLAM Building a program to map a room using LiDAR and SLAM techniques Week12
13. Final Project Combining all the topics covered in the course to build a complete AI-driven robot that can perform tasks autonomously. Week12

 

Annexure-II:

Motivational Lectures AI (Robotics)

The Rise of AI: https://www.youtube.com/watch?v=5J5bDQHQR1g

This video provides an overview of the impact that AI is having on various industries and highlights some of the breakthroughs that have been made in recent years.

 

 

How Robotics Will Change the World: https://www.youtube.com/watch?v=UwsrzCVZAb8

This video provides an overview of the impact that robotics is having on society, including in fields such as healthcare, manufacturing, and agriculture.

 

 

What is Deep Learning and How Does it Work? : https://www.youtube.com/watch?v=aircAruvnKk

This video provides a motivational introduction to deep learning, explaining what it is and how it works, as well as some of the applications of deep learning.

 

 

The Promise and Peril of Our Machine Learning Future: https://www.youtube.com/watch?v=I-JfN9HNmV4

This video provides an overview of the potential benefits and risks of machine learning, and how it will impact the future of society.

 

 

The Future of Robotics: https://www.youtube.com/watch?v=w22b-E_qP5o

This video provides an exciting look at the future of robotics, including how robots will impact various industries and the potential for robots to become a part of our daily lives.

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