An Introduction to Artificial Intelligence

3,000.00

Prof. Mausam

IIT Delhi

*Additional GST and optional Exam fee are applicable.

SKU: IIT Delhi Category:

Description

The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

INTENDED AUDIENCE

Undergraduate students in computer science

PREREQUISITES

Data Structures, Probability

INDUSTRY SUPPORT

Most software companies

ABOUT THE INSTRUCTOR

Mausam is an Associate Professor of Computer Science department at IIT Delhi, and an affiliate faculty member at University of Washington, Seattle. His research explores several threads in artificial intelligence, including scaling probabilistic planning algorithms, large-scale information extraction over the Web, and enabling complex computation over crowdsourced platforms. He received his PhD from University of Washington in 2007 and a B.Tech. from IIT Delhi in 2001. ArnetMiner, a global citation aggregator, has rated Mausam as the 25th most influential scholar in AI for 2019. He was recently awarded the AAAI Senior Member status for his long-term participation in AAAI and distinction in the field of artificial intelligence.

Additional information

Institute

IITD

Total hours

30

Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE
Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

 

Course Details

Week 1  : Introduction: Philosophy of AI, Definitions
Week 2  : Modeling a Problem as Search Problem, Uninformed Search
Week 3 : Heuristic Search, Domain Relaxations
Week 4  : Local Search, Genetic Algorithms
Week 5  : Adversarial Search
Week 6  : Constraint Satisfaction
Week 7  : Propositional Logic & Satisfiability
Week 8  : Uncertainty in AI, Bayesian Networks
Week 9  : Bayesian Networks Learning & Inference, Decision Theory
Week 10 : Markov Decision Processes
Week 11 : Reinforcement Learning
Week 12 : Introduction to Deep Learning & Deep RL

Books and References

Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, Third Edition (2009) (required).
Ian GoodFellow, Yoshua Bengio & Aaron Courville, Deep Learning, MIT Press (2016).

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