Course content (34 lessons)

1. About This Course — Artificial Intelligence — University Course This course covers Artificial Intelligence as taught in undergraduate BCA/BTech/MCA university courses. The syllabus spans 4 units —…
2. Unit 1 — Introduction to AI — Introduction to AI Artificial Intelligence (AI) is the branch of computer science concerned with building machines that can perform tasks which, when done by humans, are said to…
3. Unit 1 — Importance of AI — Importance of AI AI matters because it lets machines take over tasks that are repetitive, dangerous, data-heavy, or that require speed and consistency beyond human capability —…
4. Unit 1 — AI and its Related Fields — AI and its Related Fields AI is inherently interdisciplinary — it borrows methods and questions from several long-established fields and, in turn, feeds ideas back into them.…
5. Unit 1 — AI Techniques — AI Techniques An AI technique is a method of organising and using knowledge efficiently so that a computer program can behave intelligently, without simply enumerating every…
6. Unit 1 — Criteria for Success — Criteria for Success How do we decide whether a system is "intelligent"? AI proposes several concrete, testable criteria rather than relying on intuition alone. The Turing Test…
7. Unit 1 — Defining the Problem as a State Space Search — Defining the Problem as a State Space Search Before any search algorithm can run, a problem must be formally defined. AI formalises problem-solving as a search through a state…
8. Unit 1 — Production Systems and their Characteristics — Production Systems and their Characteristics A production system is a general framework used to implement search — it consists of rules, a working memory of current facts, and a…
9. Unit 1 — Issues in the Design of Search Problems — Issues in the Design of the Search Problem Designing a search-based problem solver is not just "pick BFS or DFS" — several deeper design questions must be answered first. Key…
10. Unit 1 — Generate-and-Test — Generate-and-Test Generate-and-test is the simplest heuristic search strategy: repeatedly generate a possible solution, then test whether it is actually a solution — keep going…
11. Unit 1 — Hill Climbing — Hill Climbing Hill climbing is generate-and-test augmented with a heuristic: at each step, move to the neighbouring state that most improves the heuristic value — like climbing a…
12. Unit 1 — Best-First Search Technique — Best-First Search Technique Best-first search combines the systematic bookkeeping of breadth-first/depth-first search with the guidance of a heuristic: at every step it expands…
13. Unit 1 — Problem Reduction (AO* Algorithm) — Problem Reduction Problem reduction solves a hard problem by breaking it into a set of smaller sub-problems, such that solving all of them (or, in an OR case, any one of them)…
14. Unit 1 — Constraint Satisfaction — Constraint Satisfaction A Constraint Satisfaction Problem (CSP) asks for an assignment of values to a set of variables such that every constraint on those variables is satisfied.…
15. Unit 2 — Definition and Importance of Knowledge — Definition and Importance of Knowledge Knowledge is organised information combined with understanding — facts, rules, and relationships that a system can use to reason, explain,…
16. Unit 2 — Approaches to Knowledge Representation — Approaches Used in Knowledge Representation There is no single "correct" way to represent knowledge inside a machine — different approaches trade off expressiveness, ease of…
17. Unit 2 — Issues in Knowledge Representation — Issues in Knowledge Representation Choosing how to represent knowledge raises several recurring design questions, regardless of which approach (semantic net, frame, logic, etc.)…
18. Unit 2 — Representing Simple Facts in Predicate Logic — Representing Simple Facts in Logic Predicate logic (first-order logic) extends propositional logic with objects, properties, relations, and quantifiers, making it expressive…
19. Unit 2 — Representing Instances and ISA Relationships — Representing Instances and ISA Relationships Much of everyday knowledge is about categories — "a canary is a bird," "a bird is an animal" — and about individual members of those…
20. Unit 2 — Computable Functions and Predicates — Computable Functions and Predicates Predicate logic becomes genuinely useful to a computer program only when its predicates and functions can be evaluated or proven — this is…
21. Unit 3 — Syntactic Processing — Syntactic Processing Natural Language Processing (NLP) is the field of AI concerned with enabling computers to understand and generate human language. Processing happens in…
22. Unit 3 — Semantic Processing — Semantic Processing Once a sentence is syntactically parsed, semantic processing determines its literal meaning — mapping words and structure onto a formal meaning representation,…
23. Unit 3 — Discourse and Pragmatic Processing — Discourse and Pragmatic Processing Sentences rarely appear alone — they occur in a discourse (a passage of connected sentences) embedded in a real-world context. The final two NLP…
24. Unit 3 — Introduction to Learning — Introduction to Learning Learning in AI is the process by which a system improves its performance on a task over time, using experience, examples, instruction, or its own…
25. Unit 3 — Rote Learning — Rote Learning Rote learning is the simplest form of machine learning: the system stores computed facts, solutions, or results directly, so that the next time the exact same…
26. Unit 3 — Learning by Taking Advice — Learning by Taking Advice Learning by advice-taking lets a system improve by accepting high-level, often imprecise instructions or suggestions from a human expert, and converting…
27. Unit 3 — Learning in Problem Solving — Learning in Problem Solving Learning in problem solving means a system improves its own problem-solving performance by reflecting on its own past attempts — successes and failures…
28. Unit 3 — Learning from Examples (Induction) — Learning from Examples — Induction Inductive learning builds a general rule or concept description from a set of labelled training examples — it is the classical ancestor of…
29. Unit 3 — Explanation-Based Learning — Explanation-Based Learning (EBL) Explanation-Based Learning generalises from just one training example — instead of needing many examples like induction, EBL uses a domain theory…
30. Unit 4 — Introduction to Expert Systems — Expert Systems: Introduction An expert system is an AI program that emulates the decision-making ability of a human expert in a specific, narrow domain, by combining a large body…
31. Unit 4 — Representing Domain-Specific Knowledge — Representing Domain-Specific Knowledge An expert system is only as good as how well its domain-specific knowledge is represented — this lesson looks at how the general KR…
32. Unit 4 — Expert System Shells — Expert System Shells Building an expert system from scratch means writing an inference engine, a user interface, and an explanation facility every single time — an expert system…
33. Unit 4 — LISP and Other AI Programming Languages — LISP and Other AI Programming Languages Classical AI systems — search programs, knowledge representation systems, and expert systems — were built with languages specifically…
34. Previous Year Questions — Artificial Intelligence — Previous Year Questions PYQ papers for this course will be added here soon. Check back later for: - End Term Examination papers - Mid Term papers -…

About This Course

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.

Continue reading: About This Course →

Frequently asked questions

Is the Artificial Intelligence course really free?

Yes. The entire Artificial Intelligence course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.

Do I get a certificate for Artificial Intelligence?

Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.

Can I run code while learning?

Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.