Schedule

Instructors		Prof. Pallab Dasgupta Prof. Partha Pratim Chakrabarti
Timing		MON (10:00–10:55), WED (8:00–9:55)
Venue		Microsoft Teams : Class Link
Teaching Assistants		Sudipa Mandal (contacttosudipamandal@gmail.com), Briti Gangopadhyay (briti_gangopadhyay@iitkgp.ac.in), Sumanta Dey (sumanta.dey@iitkgp.ac.in), Rupak Thakur (rupakthakur97@gmail.com) Vineeth Veligeti (vvveligeti@gmail.com) Ronit Samaddar(ronitsamaddar97@gmail.com) G. Chandan Ritvik(chandan.gurramluck@gmail.com)

Assignments and Exams

o	Term Project Topic Choice  28thOctober
o	Assignment on Planning [Submit in Moodle]  31st October
o	Class Test 3  7th November
o	Term Project Presentation [Video Submission][Group Member - 5][Time Limit - 20mins]  12th November
o	Class Test 4 [8AM - 9AM][Full Syllaybus]  18th November
o	Weight of components:  Tests (Best 3/4) 60%, Video+Report 20%, Two assignments 20%

Syllabus

Introduction	Course Introduction, Motivation.	[1 hour]
Problem solving by search	State Space, Problem Reduction, Game Playing, Constraint Satisfaction.	[7 hours]
Automated Reasoning	Proposition and first order logic, inference and deduction, resolution refutation, answer extraction, knowledge based systems, logic programming and constrained logic programming, non-monotonic reasoning.	[6 hours]
Planning	State-space, plan space and partial order planning, planning algorithms.	[4 hours]
Reasoning under uncertainty	Probabilistic reasoning, belief networks	[5 hours]
Learning	Inductive learning, decision trees, logical approaches, computational learning theory, neural networks, reinforcement learning, Intelligent agents, natural language understanding, Applications.	[8 hours]

Books and References

[1]	Stuart Russell, Peter Norvig, Artificial intelligence : A Modern Approach, Prentice Hall, Fourth edition, 2020.
[2]	Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan-Kaufmann, 1998.
[3]	Judea Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley Publishing Company, 1984.
[4]	Biere, A., Heule, M., Van Maaren, H., Walsh, T., Handbook of Satisfiability, IOS Press, 2009.

Online Material

Week	Topic	Chapter	PDF	PDF(Annotated)	YouTube Video	Tutorial	Attendance
Week 1	Introduction	Introduction	Introduction		Link		2nd September
	Automated Problem Solving	Automated Problem Solving	Automated Problem Solving	Automated Problem Solving	Link
Week 2		State Space Search	State Space Search	State Space Search	Link		7th September
		Heuristic Search	Heuristic Search	Heuristic Search	Link		9th September
Week 3		Game Tree Search	Game Tree Search	Game Tree Search	Link		14th September
		Local Search	Local Search		Link		16th September
Week 4	Logic and Deduction	Propositional logic	Propositional Logic	Propositional Logic	Link		21st September
		Propositional Logic to Predicate Logic	Propositional Logic to Predicate Logic	Propositional Logic to Predicate Logic	Link		23rd September
		Predicate Logic Fundamentals	Predicate Logic Fundamentals	Predicate Logic Fundamentals	Link
Week 5		Resolution Refutation	Inferencing By Resolution Refutation	Inferencing By Resolution Refutation	Link		28th September
		Constraint Satisfaction Problems	Constraint Satisfaction Problems	Constraint Satisfaction Problems	Link	Assignment (Moodle)	30th September
Week 6	Planning in AI	Planning in AI Part 1	Planning in AI (Combined)		Link		5th October
		Planning in AI Part 2		Planning in AI	Link	PDDL Hands On Resources/Solutions	7th October
Week 7		Planning in AI Part 3		Planning in AI 3	Link	Assignment (Moodle)	12th October
	Reasoning Under Uncertainty	Reasoning Under Uncertainty Part 1	Reasoning under Uncertainty(combined)	Reasoning Under Uncertainty Part 1	Link		14th October
Week 8		Reasoning Under Uncertainty Part 2		Reasoning Under Uncertainty Part 2	Link		19th October
		Reasoning Under Uncertainty Part 3			Link	Netica Hands On Resources/Solutions	21st October
Week 9	Learning	Machine Learning Fundamentals	Machine Learning Fundamentals		Link		28th October
Week 10		Learning Decision Trees	Learning Decision Trees		Link		2nd November
		Neural Networks	Neural Networks and Deep Learning		Link		4th November
Week 11		Deep Learning Fundamentals	Neural Networks and Deep Learning		Link		9th November
		Reinforcement Learning Fundamentals	Reinforcement Learning Fundamentals		Link		11th November

Previous course pages: 2019 | 2018