There are four modules of AI Courses. Students can opt for any of these as per their interest and eligibility.

1. Introduction to Artificial Intelligence (Basic)

• What is AI? Definition and History
• Basic Concepts: Machine Learning, Deep Learning, and Neural Networks
• Introduction to Data Science and Big Data
• Common AI Algorithms and Techniques
• Introduction to Programming for AI: Python Basics
• AI Applications: Healthcare, Finance, and Entertainment
• Ethical Considerations and Future Trends
• Project: Implementing a Simple AI Model
  
  

2. Intermediate AI Techniques

• Supervised vs. Unsupervised Learning
• Regression and Classification Algorithms
• Clustering Techniques: K-means, Hierarchical
• Dimensionality Reduction: PCA, LDA
• Introduction to Neural Networks and Deep Learning
• Model Evaluation and Validation Techniques
• Handling Large Datasets and Feature Engineering
• Project: Building and Tuning a Machine Learning Model

3. Advanced AI and Machine Learning

• Advanced Neural Networks: CNNs, RNNs
• Deep Learning Frameworks: TensorFlow, PyTorch
• Natural Language Processing: Text Classification, Sentiment Analysis
• Reinforcement Learning: Basics and Applications
• Generative Adversarial Networks (GANs)
• AI in Robotics and Automation
• AI Deployment and Scaling Solutions
• Project: Developing an Advanced AI Solution

4. AI in Real-World Applications (Capstone)

• Case Studies: AI in Healthcare, Finance, and Retail
• End-to-End AI Project: From Data Collection to Model Deployment
• Integrating AI with Cloud Platforms
• Addressing Real-World Challenges: Bias, Privacy, and Security
• Building AI Solutions for Specific Industry Problems
• Collaborative Projects and Team Work
• Preparing and Presenting Your AI Solution
• Final Project Presentation and Evaluation

Introduction to Robotics

• Overview of Robotics: Definition, history, and evolution
• Types of Robots: Industrial robots, service robots, and autonomous vehicles
• Applications of Robotics: Manufacturing, healthcare, exploration, and more
• Ethics and Future Trends: Impact of robotics on society and future advancements
  
  

Basic Concepts in Robotics

• Robotic Systems and Components: Sensors, actuators, controllers, and power sources
• Kinematics and Dynamics: Robot motion, forward and inverse kinematics
• Robot Programming Basics: Introduction to programming languages used in robotics (e.g., Python, C++)
  
  

Robotics Hardware

• Design and Construction: Understanding mechanical components and assembly
• Sensor Integration: Types of sensors and their applications
• Actuators and Motors: Functionality and selection criteria
• Control Systems: Basics of feedback and control systems
  
  

Robotics Software

• Programming Robots: Writing and debugging robot code
• Simulation Tools: Using software for designing and testing robotic systems
• Artificial Intelligence in Robotics: Machine learning and its applications in robotics
  
  

Advanced Robotics Topics

• Autonomous Systems: Navigation, path planning, and decision-making
• Human-Robot Interaction: Interfaces and collaboration techniques
  
  

Robotic Vision Systems: Computer vision and image processing

Introduction to Computer Vision

• What is Computer Vision?
• Historical Background and Evolution
• Applications and Use Cases
• Overview of Key Technologies
  
  

Basic Image Processing

• Image Representation and Data Formats
• Fundamental Operations: Filtering, Convolution
• Color Spaces and Image Enhancement Techniques
  
  

Feature Detection and Matching

• Edge Detection and Corner Detection
• Feature Extraction Techniques
• Feature Matching Algorithms: SIFT, SURF, ORB
  
  

Object Detection

• Introduction to Object Detection
• Background Subtraction Techniques
• Object Detection Algorithms: YOLO, SSD, Faster R-CNN
  
  

Image Classification

• Concepts of Image Classification
• Deep Learning for Classification
• Convolutional Neural Networks (CNNs): Architecture and Training
  
  

Image Segmentation

• Techniques for Image Segmentation
• Thresholding, Clustering, and Region-Based Methods
• Advanced Segmentation with Deep Learning
  
  

Advanced Computer Vision Techniques

• Image and Video Analysis
• 3D Vision and Depth Estimation
• Motion Detection and Tracking
  
  

Machine Learning and Computer Vision

• Integration of Machine Learning with Computer Vision
• Supervised and Unsupervised Learning Methods
• Practical Applications in Object Recognition
  
  

Real-Time Computer Vision

• Techniques for Real-Time Processing
• Optimizing Performance for Speed and Accuracy
• Real-World Applications: Robotics, Autonomous Vehicles
  
  

Practical Applications and Case Studies

• Case Studies in Various Domains: Healthcare, Security, Retail
• Project Work: Implementing a Computer Vision System
  
  

Tools and Libraries

• Introduction to Computer Vision Libraries: OpenCV, TensorFlow, PyTorch
• Practical Exercises Using Popular Tools
• Integration with Other Technologies

Introduction to Machine Learning

• Overview of Machine Learning
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Applications and Impact of Machine Learning
  
  

Data Preprocessing and Exploration

• Data Collection and Cleaning
• Feature Selection and Engineering
• Exploratory Data Analysis (EDA)
• Handling Missing Data and Outliers
  
  

Supervised Learning: Regression

• Introduction to Regression Algorithms
• Linear Regression: Theory and Implementation
• Polynomial Regression and Regularization
• Model Evaluation Metrics: MSE, RMSE, R²
  
  

Supervised Learning: Classification

• Classification Algorithms Overview
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
  
  

Unsupervised Learning: Clustering

• Introduction to Clustering Techniques
• K-Means Clustering
• Hierarchical Clustering
• Evaluation Metrics for Clustering
  
  

Unsupervised Learning: Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Feature Reduction and Visualization Techniques
  
  

Neural Networks and Deep Learning

• Basics of Neural Networks
• Introduction to Deep Learning
• Convolutional Neural Networks (CNNs) for Image Classification
• Recurrent Neural Networks (RNNs) for Sequence Data
  
  

Model Evaluation and Hyperparameter Tuning

• Cross-Validation Techniques
• Grid Search and Random Search
• Overfitting and Underfitting
• Model Selection and Performance Metrics
  
  

Advanced Machine Learning Techniques

• Ensemble Methods: Bagging and Boosting
• Gradient Boosting Machines (GBM)
• XGBoost and LightGBM
  
  

Deployment and Production

• Model Deployment Strategies
• API Integration for Machine Learning Models
• Monitoring and Maintaining Deployed Models
  
  

Ethical Considerations and Future Trends

• Ethical Implications of Machine Learning
• Fairness and Bias in Machine Learning Models
• Emerging Trends and Future Directions in AI

• 1. Introduction to Python

  • Overview of Python: History, features, and applications.
  • Setting Up the Environment: Installation of Python and IDEs (PyCharm, Jupyter Notebook).
  • Hello World: Writing and running your first Python program.

  2. Python Basics

  • Syntax and Semantics: Understanding indentation, comments, and basic syntax rules.
  • Data Types and Variables: Integers, floats, strings, and boolean values.
  • Operators: Arithmetic, comparison, logical, and assignment operators.

  3. Control Flow

  • Conditional Statements: if, elif, and else.
  • Loops: for and while loops, loop control statements (break, continue).
  • List Comprehensions: Efficient looping and list generation.

  4. Functions and Modules

  • Defining Functions: Function syntax, parameters, and return values.
  • Built-in Functions: Commonly used built-in functions and their applications.
  • Modules and Packages: Importing and using modules, creating your own modules.

  5. Data Structures

  • Lists: Operations, methods, and slicing.
  • Tuples: Immutability and use cases.
  • Dictionaries: Key-value pairs, methods, and dictionary comprehension.
  • Sets: Unique elements, set operations.

  6. File Handling

  • Reading Files: Opening, reading, and closing files.
  • Writing Files: Writing data to files, appending.
  • File Manipulation: Handling different file formats (text, CSV).

  7. Error Handling and Exceptions

  • Exception Handling: Try, except, finally blocks.
  • Raising Exceptions: Custom exceptions and error handling best practices.

  8. Object-Oriented Programming (OOP)

  • Classes and Objects: Defining classes, creating objects.
  • Inheritance: Basic inheritance concepts and usage.
  • Polymorphism and Encapsulation: Method overriding, private attributes.

  9. Libraries and Frameworks

  • Standard Library Overview: Commonly used libraries (math, datetime, etc.).
  • NumPy: Arrays and mathematical functions.
  • Pandas: Data manipulation and analysis.
  • Matplotlib/Seaborn: Data visualization.

  10. Advanced Topics (Optional)

  • Concurrency: Introduction to threading and multiprocessing.
  • Data Science Overview: Machine learning basics with Python.
  • Testing and Debugging: Unit testing with unittest, debugging techniques.