AI (Robotics) Course Outline

Course Contents / Lesson Plan

Course Title: AI (Robotics)

Duration: 3 Months

MODULES

Scheduled Weeks	Module Title	Days	Hours	Learning Units
Week 1	Introduction to AI and Robotics	Day 1	Hour 1	Course Introduction and Expectations
			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
			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
			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
			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)
			Hour 2	Text preprocessing and cleaning
			Hour 3	Tokenization and stemming
			Hour 4	Part-of-speech tagging
		Day2	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)
			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)
			Hour 2	Artificial Neural Networks (ANNs)
			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
			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
			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	Autonomous Driving
			Hour 4	Autonomous Driving
Week 10	Advanced Computer Vision	Day 1	Hour 1	Introduction to Advanced Computer Vision
			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)
			Hour 2	DRL Frameworks
			Hour 3	DRL Frameworks
			Hour 4	DQN Revisited
		Day 2	Hour 1	REINFORCE
			Hour 2	Actor-Critic Methods
			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
			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	Week 10
11	DRL Techniques	Build a robot that can learn to perform complex tasks using DRL techniques	Week 11
12	LiDAR and SLAM	Building a program to map a room using LiDAR and SLAM techniques	Week 12
13	Final Project	Combining all the topics covered in the course to build a complete AI-driven robot that can perform tasks autonomously.	Week 13

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.

AI (Robotics) Outline

AI (Robotics) Course Outline

Course Contents / Lesson Plan

Course Title: AI (Robotics)

Duration: 3 Months

MODULES

Tasks for Certificate in AI (Robotics)

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.