Module 1: Introduction to Artificial Intelligence

1.1  Overview of AI

• Definition and key concepts of AI
• History and evolution of AI
• AI vs Machine Learning vs Deep Learning

1.2  Applications of AI

• AI in various industries (healthcare, finance, retail, etc.)
• Case studies of successful AI implementations

1.3  AI Ethics and Governance

• Ethical considerations in AI
• AI regulations and standards

1.4  Future Trends in AI 

• Emerging technologies and future directions
• AI research and development

Module 2: Machine Learning Fundamentals

2.1  Introduction to Machine Learning 

• Definition and key concepts
• Types of machine learning: supervised, unsupervised, reinforcement learning

2.2  Supervised Learning 

• Overview and key algorithms (linear regression, decision trees, )
• Practical examples and use cases

2.3  Unsupervised Learning

• Overview and key algorithms (k-means clustering, PCA, )
• Practical examples and use cases

2.4  Reinforcement Learning

• Key concepts and algorithms (Q-learning, deep Q-networks)
• Practical examples and use cases

2.5  Evaluation and Model Selection

• Model evaluation metrics (accuracy, precision, recall, )
• Cross-validation and model selection techniques

Module 3: Deep Learning

3.1  Introduction to Deep Learning

• Key concepts and differences from machine learning
• Overview of neural networks

3.2  Neural Networks and Training

• Structure and functioning of neural networks
• Training neural networks: backpropagation, gradient descent

3.3  Convolutional Neural Networks (CNNs)

• Key concepts and architecture
• Applications in image processing

3.4  Recurrent Neural Networks (RNNs)

• Key concepts and architecture
• Applications in sequence prediction and natural language processing

3.5  Practical Deep Learning

• Hands-on exercises with deep learning frameworks (TensorFlow, Keras, )

Module 4: Natural Language Processing (NLP)

4.1  Introduction to NLP

• Key concepts and applications
• NLP challenges and opportunities

4.2  Text Processing and Feature Extraction

• Tokenization, stemming, lemmatization
• Feature extraction techniques: TF-IDF, word embeddings

4.3  Sentiment Analysis and Text Classification

• Building and evaluating sentiment analysis models
• Practical examples and case studies

4.4  Advanced NLP Techniques

• Named entity recognition, machine translation
• Recent advancements in NLP: BERT, GPT,

4.5  Practical NLP

• Hands-on exercises with NLP libraries (NLTK, spaCy, )

Module 5: AI in Computer Vision

5.1  Introduction to Computer Vision

• Key concepts and applications
• Challenges in computer vision

5.2  Image Processing Techniques

• Image filtering, edge detection, feature extraction
• Practical examples and case studies

5.3  Object Detection and Recognition

• Key algorithms: YOLO, R-CNN,
• Applications and use cases

5.4  Image Segmentation

• Techniques and algorithms
• Practical examples and case studies

5.5  Practical Computer Vision

• Hands-on exercises with computer vision libraries (OpenCV, )

Module 6: Introduction to Prompt Engineering

6.1  Overview of Prompt Engineering

• Definition and significance
• Role in AI and NLP
• Differences between general prompt creation and prompt engineering

6.2  Applications and Use Cases

• Examples of prompt engineering in various AI applications
• How prompt engineering improves AI performance and user interaction

6.3  Understanding Prompts

• What is a prompt?
• Types of prompts (questions, statements, commands, etc.)

6.4  Crafting Effective Prompts

• Characteristics of effective prompts
• Best practices for prompt design

6.5  Common Pitfalls and How to Avoid Them

• Mistakes in prompt engineering
• Strategies to overcome common issues

6.6  Introduction to ChatGPT

• Overview of ChatGPT and its capabilities
• How ChatGPT uses prompts

6.7  Dynamic Prompting

• Creating dynamic and adaptive prompts
• Techniques for handling diverse user inputs

6.8  Real-World Scenarios

• Case studies and examples of prompt engineering in action
• Practical applications in customer service, education, content generation, etc.

Module 7: AI in Business

7.1  AI Strategy and Implementation

• Developing an AI strategy for businesses
• Key considerations for AI implementation

7.2  AI in Marketing and Sales

• Applications of AI in customer segmentation, personalization, etc.
• Case studies and examples

7.3  AI in Operations and Supply Chain

• Applications of AI in predictive maintenance, inventory management,
• Case studies and examples

7.4  AI in Finance and Risk Management

• Applications of AI in fraud detection, credit scoring, etc.
• Case studies and examples

Module 8: Hands-on Lab Sessions

8.1  Setting Up AI Development Environment

• Installing and configuring AI frameworks (TensorFlow, PyTorch, etc.)
• Overview of development tools and platforms

8.2  Data Collection and Preprocessing

• Techniques for data collection and preprocessing
• Practical exercises

8.3  Building and Training Models

• Developing machine learning and deep learning models
• Practical exercises with real-world datasets

8.4  Model Evaluation and Optimization

• Techniques for model evaluation and optimization
• Practical exercises and case studies

8.5  Deployment and Integration

• Deploying AI models in production environments
• Integration with existing systems

8.6  Capstone Project

• Developing and presenting a comprehensive AI project
• Applying learned concepts to solve real-world problems

Open to undergraduate students of all streams.