Course Name: An Introduction to Artificial Intelligence

Course abstract

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.


Course Instructor

Media Object

Prof. Mausam

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.
More info

Teaching Assistant(s)

Deepanshu Jindal

BE/B.Tech

 Course Duration : Jan-Apr 2020
  View Course
 Enrollment : 18-Nov-2019 to 03-Feb-2020
 Exam registration : 16-Dec-2019 to 20-Mar-2020
 Exam Date : 26-Apr-2020

Enrolled

39408

Registered

996

Certificate Eligible

176

Certified Category Count

Gold

0

Silver

0

Elite

27

Successfully completed

149

Participation

729

Success

Elite

Silver

Gold




Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
An Introduction to Artificial Intelligence - Toppers list
Top 1 % of Certified Candidates

SAI SANDEEP MUTYALA 71%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)

GOVIND ASAWA 69%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)


Top 2 % of Certified Candidates

KODAKANDLA ACHARYA TARUN 68%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)

GUNDLA KEERTHI 67%

JNTU HYDERBAD COLLEGE OF ENGINEERING,JAGTIAL


Top 5 % of Certified Candidates

ABHISHEK YELISETTI 66%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)

R SAI NIVAS 66%

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

MANOJ KARUNAKARAN NAMBIAR 65%

Tata Consultancy Services Ltd

MANOJ GUPTA GUDLA 65%

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

RAMYA R 64%

KAMARAJ COLLEGE OF ENGINEERING AND TECHNOLOGY

AYUSH NATH JHA 64%

NATIONAL INSTITUTE OF TECHNOLOGY PATNA

VINEETH PATIL 64%

MAHATMA GANDHI INSTITUTE OF TECHNOLOGY

DHUNDE VAMSHI KRISHNA 64%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)

Enrollment Statistics

Total Enrollment: 39408

Registration Statistics

Total Registration : 3194

Assignment Statistics



One of the content-rich courses available in NPTEL. Attempt to such a collection of contents from any book or external sources is almost impossible. Course must be preferably advertised by NPTEL. It covers probability, statistics and many of fundamental machine learning algorithms - all with detailed examples both with python and solving-by-hand. The work made by Professor towards course preparation seems to be very sincere. I thank NPTEL for this great course and this MOOC approach.


Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.