Master the core principles of artificial intelligence through this comprehensive 40-hour course covering both symbolic and machine learning approaches. Explore foundational AI concepts including definitions, historical development, and real-world applications while learning to distinguish between symbolic and machine learning paradigms. Develop practical skills by solving classical search problems and understanding basic logic, knowledge representation, and fuzzy logic concepts. Analyze various problem scenarios to determine the most appropriate machine learning approach—whether supervised, unsupervised, or reinforcement learning—and justify your selections using proper AI terminology. Build a solid foundation in AI fundamentals that prepares you for advanced study and practical application in artificial intelligence fields.
Overview
Syllabus
- Explain foundational AI concepts—definitions, history, applications—and differentiate symbolic vs. ML paradigms.
- Apply core symbolic AI ideas by solving a small classical search problem and describe basic logic/knowledge-representation/fuzzy concepts.
- Analyze simple problem scenarios and select an appropriate ML paradigm (supervised / unsupervised / reinforcement), justifying the choice with correct terminology.
Taught by
Centre for Academic Advancement and Flexible Learning