Artificial Intelligence

Module 1: Introduction to Artificial Intelligence

• Definition, Scope, and Importance of AI
• History and Evolution of AI
• Applications of AI in Various Domains (Healthcare, Finance, Robotics, etc.)
• AI vs. Human Intelligence
• Ethical and Societal Considerations in AI

Module 2: Problem Solving and Search Techniques

• Problem-Solving Approach in AI
• State Space Representation and Search Trees
• Uninformed Search Strategies: Breadth-First Search (BFS), Depth-First Search (DFS)
• Informed Search Strategies: A*, Best-First Search, Hill Climbing
• Constraint Satisfaction Problems (CSP)

Module 3: Knowledge Representation and Reasoning

• Introduction to Knowledge Representation
• Logic-Based Knowledge Representation: Propositional and Predicate Logic
• Rule-Based Systems: Forward and Backward Chaining
• Semantic Networks, Frames, and Ontologies
• Probabilistic Reasoning and Bayesian Networks

Module 4: Machine Learning Fundamentals

• Introduction to Machine Learning and its Relationship with AI
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Training, Testing, and Validation of Models
• Overfitting and Underfitting Issues
• Performance Metrics for Machine Learning Models

Module 5: Supervised and Unsupervised Learning

• Supervised Learning Algorithms:
  • Linear and Logistic Regression
  • Decision Trees and Random Forest
  • Support Vector Machines (SVM)
  • Neural Networks (Basic Introduction)
• Unsupervised Learning Algorithms:
  • Clustering Techniques: K-Means, DBSCAN, Hierarchical Clustering
  • Dimensionality Reduction Techniques: PCA, t-SNE

Module 6: Deep Learning and Neural Networks

• Introduction to Deep Learning
• Artificial Neural Networks (ANN) Architecture
• Activation Functions and Optimization Techniques
• Backpropagation and Gradient Descent
• Convolutional Neural Networks (CNN) for Image Processing
• Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)

Module 7: Natural Language Processing (NLP)

• Basics of NLP and its Applications
• Text Preprocessing: Tokenization, Lemmatization, Stopword Removal
• Part-of-Speech (POS) Tagging and Named Entity Recognition (NER)
• Word Embeddings: Word2Vec, GloVe
• Transformers and Large Language Models (LLMs)

Module 8: Computer Vision and Image Processing

• Basics of Image Processing and Computer Vision
• Image Classification, Object Detection, and Segmentation
• Feature Extraction and Feature Matching
• Pretrained Models: VGG, ResNet, YOLO
• Applications of AI in Image and Video Processing

Module 9: Reinforcement Learning

• Basics of Reinforcement Learning (RL)
• Markov Decision Process (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Policy-Based and Value-Based Methods
• Applications of RL in Gaming, Robotics, and Autonomous Systems

Module 10: AI in Robotics and Automation

• AI in Robotics: Basics and Applications
• Robotic Perception and Motion Planning
• Pathfinding Algorithms: Dijkstra, A* Algorithm
• Human-Robot Interaction (HRI)
• AI-Enabled Automation and Industry 4.0

Module 11: AI Tools, Platforms, and Frameworks

• Introduction to AI Programming Languages (Python, R)
• Popular AI Libraries and Frameworks: TensorFlow, PyTorch, Scikit-learn
• Cloud AI Platforms: Google AI, AWS AI, Microsoft Azure AI
• AI Model Deployment and Optimization

Module 12: AI Ethics and Future Trends

• Ethical Considerations in AI: Bias, Fairness, and Privacy
• AI Regulations and Policies
• Future of AI: Explainable AI (XAI), Quantum AI, General AI
• AI in Sustainable Development and Social Good
• Case Studies and Real-World AI Applications