Fundamentals Of Artificial Intelligence

3,000.00

Prof. Shyamanta M. Hazarika

IIT Guwahati

*Additional GST and optional Exam fee are applicable.

SKU: IIT Guwahati Category:

Description

What does automatic scheduling or autonomous driving have in common with web search, speech recognition, and machine translation? These are complex real-world problems that span across various practices of engineering! Aim of artificial intelligence (AI) is to tackle these problems with rigorous mathematical tools. The objective of this course is to present an overview of the principles and practices of AI to address such complex real-world problems. The course is designed to develop a basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI.

INTENDED AUDIENCE

Final Year B.Tech; M.Tech and PhD

PREREQUISITES

Basic Course in Probability and Linear Algebra

ABOUT THE INSTRUCTOR

Shyamanta M Hazarika is a Professor of Mechanical Engineering at IIT Guwahati and leads the Biomimetic Robotics and Artificial Intelligence Lab. His research interest is in Rehabilitation Robotics. This translates into interest in Artificial Intelligence, Biomimetic Robotics and Robotic Neurorehabilitation. Prior to joining IIT Guwahati, he was with the Department of Computer Science and Engineering, Tezpur University. He has been a Vertretungsprofessur of Cognitive Systems and Neuro Informatics, University of Bremen, Germany. Prof. Hazarika holds a B.E. in Mechanical Engineering from Assam Engineering College, Guwahati, India; M.Tech. in Robotics from Center for Robotics, IIT Kanpur, India. He completed his PhD in Artificial Intelligence (Knowledge Representation and Reasoning) from School of Computing, University of Leeds, England.

 

 

Additional information

Institute

IITG

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:    AI and Problem Solving by Search
Week 2:    Problem Solving by Search
Week 3:    Problem Solving by Search
Week 4:    Knowledge Representation and Reasoning
Week 5:    Knowledge Representation and Reasoning
Week 6:    Knowledge Representation and Reasoning
Week 7:    Reasoning under Uncertainty
Week 8:    Planning
Week 9:    Planning and Decision Making
Week 10:  Machine Learning
Week 11:  Machine Learning
Week 12:  Machine Learning

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