Artificial Intelligence and Machine Learning
Module 1: Foundations of AI and ML
- Historical perspective
- Definition and goals of AI
- AI applications in real-world scenarios
- Linear algebra
- Probability and statistics
- Basics of data structures and algorithms
Module 2: Machine Learning Fundamentals
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
Module 3: Deep Learning
- Neural Networks
- Deep Learning Architectures
Module 4: Advanced Machine Learning Techniques
- Ensemble Learning
- Reinforcement Learning
- Time Series Analysis
Module 5: Applications of AI and ML
- Natural Language Processing (NLP)
- Computer Vision Applications
- Real-world Case Studies
Module 6: Ethical Considerations and Future Trends
- Ethical Issues in AI
- Future Trends in AI and ML
Applications of AI/ML
- Healthcare: Diagnosis, personalized medicine, predictive analytics.
- Finance: Fraud detection, algorithmic trading, credit scoring.
- Retail: Recommendation systems, demand forecasting, supply chain optimization.
- Automotive: Autonomous vehicles, driver assistance systems.
- Natural Language Processing (NLP): Chatbots, language translation, sentiment analysis.
- Computer Vision: Facial recognition, object detection, autonomous drones.
- Manufacturing: Predictive maintenance, quality control.
- Education: Adaptive learning systems, intelligent tutoring.
