Artificial intelligence (AI)

Course Title: Artificial Intelligence (AI)

Module 1: Foundations of Artificial Intelligence

Unit 1: Introduction to AI

• Definition and Scope of AI
• Historical Developments and Milestones
• Ethical Considerations in AI

Unit 2: Types of AI

• Narrow AI vs. General AI
• Symbolic AI and Statistical AI
• Hybrid Approaches in AI

Unit 3: AI in Society

• Impact of AI on Society and Economy
• Regulatory and Ethical Frameworks
• Responsible AI Development

Module 2: Machine Learning Fundamentals

Unit 1: Basics of Machine Learning

• Types of Machine Learning (Supervised, Unsupervised, and Reinforcement)
• Model Selection and Evaluation Metrics
• Overfitting and Underfitting

Unit 2: Advanced Supervised Learning

• Ensemble Learning Techniques (Random Forests, Gradient Boosting)
• Support Vector Machines (SVM)
• Model Interpretability and Explainability

Unit 3: Advanced Unsupervised Learning

• Dimensionality Reduction Techniques (PCA, t-SNE)
• Anomaly Detection
• Generative Adversarial Networks (GANs)

Module 3: Deep Learning and Neural Networks

Unit 1: Neural Networks Architecture

• Introduction to Deep Neural Networks
• Feedforward and Recurrent Neural Networks
• Architectural Considerations in Deep Learning

 

Unit 2: Advanced Deep Learning

• Advanced Activation Functions and Optimization Algorithms
• Attention Mechanisms
• Neural Architecture Search (NAS)

Unit 3: Transfer Learning and Pre-trained Models

 

• Transfer Learning Concepts
• Fine-tuning Pre-trained Models
• Case Studies and Applications

Module 4: Natural Language Processing (NLP)

Unit 1: Basics of NLP

• Overview of Natural Language Processing
• Tokenization, Stemming, and Lemmatization
• Named Entity Recognition (NER)

Unit 2: Sentiment Analysis and Text Classification

• Analyzing Sentiments in Text
• Text Classification Techniques
• Building NLP Models for Real-World Applications

Unit 3: Language Models and Transformers

• Introduction to Language Models
• Transformer Architecture
• BERT and GPT Models

Module 5: Computer Vision

Unit 1: Introduction to Computer Vision

• Basics of Image Processing
• mage Classification and Object Detection
• mage Segmentation

Unit 2: Convolutional Neural Networks (CNNs) in Computer Vision

• CNNs for Image Recognition
• Transfer Learning in Computer Vision
• Applications of CNNs in Healthcare, Autonomous Vehicles, etc.

Unit 3: Advanced Computer Vision Applications

• Image Generation with GANs
• Facial Recognition and Emotion Detection
• Object Tracking and Scene Understanding

 

Module 6: Reinforcement Learning

Unit 1: Basics of Reinforcement Learning

• Markov Decision Processes
• Rewards, Policies, and Value Functions
• Exploration vs. Exploitation

Unit 2: Q-Learning and Policy Gradient Methods

• Q-Learning Algorithm
• Policy Gradient Methods
• Applications of Reinforcement Learning in Robotics and Games

Unit 3: Deep Reinforcement Learning

• Deep Q Networks (DQN)
• Deep Policy Gradient Methods
• Challenges and Future Directions in Reinforcement Learning

Module 7: AI in Practice

Unit 1: AI Model Deployment

• Strategies for Model Deployment
• Containerization and Docker
•  Cloud-based Deployment Services

Unit 2: Responsible AI

• • Fairness and Bias Mitigation in AI
  • Explainability and Interpretability

• AI Governance and Regulations

Unit 3: Industry Applications and Case Studies

• AI in Healthcare, Finance, and E-commerce
• Real-world AI Success Stories
• Challenges and Lessons Learned

Module 8: Capstone Project

Unit 1: Project Definition and Planning

• Defining a Real-world AI Problem
• Project Scope and Objectives
• Resource and Technology Requirements

Unit 2: Project Implementation

• Data Collection and Preprocessing
• Model Development and Training
• Evaluation and Fine-tuning