Artificial Intelligence — University Course

This course covers Artificial Intelligence as taught in undergraduate BCA/BTech/MCA university courses. The syllabus spans 4 units — the foundations and search methods that let a machine solve problems, the representation schemes that let it hold knowledge, the language and learning techniques that let it improve and communicate, and the expert systems and languages that put all of it into practice.

Syllabus at a Glance:

What you will learn:

• How to frame a real-world problem as a state space and search it systematically with production systems
• Heuristic techniques — hill climbing, best-first search (A), problem reduction (AO), and constraint satisfaction — that make search tractable
• How knowledge is represented inside a machine — semantic networks, frames, scripts, production rules, and formal predicate logic
• How a machine processes human language at the syntactic, semantic, discourse and pragmatic levels
• The major paradigms of machine learning that predate deep learning — rote learning, learning by advice, learning in problem solving, induction, and explanation-based learning
• What an expert system is, how it represents domain knowledge, and how LISP became the language of classical AI

PYQ papers are available at the end of the lesson list.