Schedule

Instructors: Abhijit Das and Aritra Hazra
Timing: Slot F3 [Wed (10:00am–11:00am), Thu (09:00am–10:00am), Fri (11:00am–12:00pm)]
Classroom: NC234
Teaching Assistants: Akash Kundu, Debranjan Pal, Hritaban Ghosh

Notices and Announcements

August 01, 2023 [for applicants]

We have processed the requests. The approved students may please go ahead with their registrations for this course.

July 27, 2023 [for prospective registrants]

Students are requested to apply for this course using the ERP portal. Because of limited capacity of seats, we may have to apply some screening during the approval of students requests. We will do that near the middle or the end of the next week. Please do not send individual emails to us. We cannot respond to each and every email that we receive about this matter. Please do not ask about the timelines. We will give you sufficient time to switch to other courses, in case we are unable to accept you in this course.

Tentative Coverage

• Introduction: History of AI, definitions and notions, automated problem solving
• State Space Search: Uninformed search, informed search, problem reduction search
• Adversarial Search: Game trees, minimax algorithm, alpha-beta pruning
• Local Search: Hill climbing, simulated annealing, Genetic algorithms, Ant colony optimization
• Constraint Satisfaction Problem
• Logical Deduction: Propositional and predicate logic, resolution refutation
• Planning: Partial order, graph, SAT
• Uncertainty: Bayesian inference, Conditional dependence
• Machine Learning: Decision trees, regression, neural networks
• Reinforcement learning: Model-based and model-free learning
• Deep Learning: Convolution and recurrent networks

Slides

• Introduction: What is AI? | Timeline (Animated)
• Search techniques: State space | Uninformed search | Informed search | AND-OR search | Adversarial search | Local search | Constraint satisfaction problems
• Reasoning and planning: Propositional logic | Predicate logic | Planning | Probabilistic reasoning
• Machine learning: Decision trees (source) | Part 1 (source)

Books and References

The main textbook will be:

• [AIMA]  Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, fourth global edition, Pearson, 2022.

Some other references are:

• [AINS]  Nils J Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998.
• [POAI]  Nils J Nilsson, Principles of Artificial Intelligence, Morgan Kaufmann, 1980.
• [RKNAI]  Elaine Rich, Kevin Knight, and Shivashankar B. Nair, Artificial Intelligence, third edition, Tata McGraw-Hill, 2009.
• David L Poole and Alan K Mackworth, Artificial Intelligence: Foundations of Computational Agents, third edition, Cambridge University Press, 2023.

Test Schedule (Tentative)

• Class Test 1: 31-Aug-2023 [Questions with solutions]
• Class Test 2: 06-Nov-2023 [Questions with solutions]
• Mid-semester Test: 25-Sep-2023 [Questions with solutions]
• End-semester Test: 23-Nov-2023 [Questions with solutions]