- Introduction to Artificial Intelligence
- Definition and history of AI
- Types of AI: narrow vs. general AI
- Machine Learning Basics
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural Networks and Deep Learning
- Structure of neural networks
- Activation functions
- Backpropagation
- Natural Language Processing (NLP)
- Tokenization and parsing
- Sentiment analysis
- Machine translation
- Computer Vision
- Image classification
- Object detection
- Facial recognition
- Robotics and AI
- Autonomous systems
- Sensor fusion
- Path planning algorithms
- Expert Systems and Knowledge Representation
- Rule-based systems
- Semantic networks
- Ontologies
- Ethical AI and Bias
- Fairness in machine learning
- Transparency and explainability
- AI governance frameworks
- AI Hardware and Infrastructure
- GPUs and TPUs
- Quantum computing for AI
- Edge AI and IoT
- Future of AI
- AGI (Artificial General Intelligence)
- Challenges and limitations
- Emerging AI technologies
Resources:
- Online courses: Coursera, edX, Udacity
- Books: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig
- Research papers: arXiv.org (AI section)
- Conferences: NeurIPS, ICML, ICLR