Course Name: Essential Mathematics for Machine Learning

Course abstract

Machine learning (ML) is one of the most popular topics of nowadays research. This particular topic is having applications in all the areas of engineering and sciences. Various tools of machine learning are having a rich mathematical theory. Therefore, in order to develop new algorithms of machine/deep learning, it is necessary to have knowledge of all such mathematical concepts. In this course, we will introduce these basic mathematical concepts related to the machine/deep learning. In particular, we will focus on topics from matrix algebra, calculus, optimization, and probability theory those are having strong linkage with machine learning. Applications of these topics will be introduced in ML with help of some real-life examples.


Course Instructor

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Prof. Sanjeev Kumar

Prof. Sanjeev Kumar is working as an associate professor with Department of Mathematics, IIT Roorkee. Earlier, he worked as a postdoctoral fellow with Department of Mathematics and Computer Science, University of Udine, Italy and assistant professor with IIT Roorkee. He is actively involved in teaching and research in the area of computational algorithms, inverse problems and image processing. He has published more than 55 papers in various international journals conferences of repute. He has completed a couple of sponsored research projects and written several chapters in reputed books published with Springer and CRC press. So far, he has completed three MOOC courses namely, Numerical Methods, Multivariable Calculus and Matrix Analysis with Applications under NPTEL program.
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Prof. S.K. Gupta

Prof. S. K. Gupta is an Associate Professor in the Department of Mathematics, IIT Roorkee. His area of expertise includes nonlinear, non-convex and Fuzzy optimization. He has guided three PhD thesis and have published more than 40 papers in various international journals of repute. He has developed four courses for NPTEL in the area of Mathematics.
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Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Sep-Nov 2020
  View Course
 Enrollment : 20-May-2020 to 21-Sep-2020
 Exam registration : 14-Sep-2020 to 02-Nov-2020
 Exam Date : 19-Dec-2020

Enrolled

7205

Registered

285

Certificate Eligible

160

Certified Category Count

Gold

10

Silver

47

Elite

60

Successfully completed

43

Participation

51

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 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Essential Mathematics for Machine Learning - Toppers list
Top 1 % of Certified Candidates

RASHMITA HORE 96%

Pondicherry University

SHREYAS SUNIL JADHAV 94%

THAKUR COLLEGE OF ENGINEERING AND TECHNOLOGY


Top 2 % of Certified Candidates

B THILAKA 92%

SRI VENKATESWARA COLLEGE OF ENGINEERING

DR ANU BALA 92%

Multani Mal Modi College


Top 5 % of Certified Candidates

PRIYANKA MAJUMDER 91%

TECHNO COLLEGE OF ENGINEERING AGARTALA

ASHUTOSH PANDEY 90%

THAKUR COLLEGE OF ENGINEERING AND TECHNOLOGY

ANURAG TANWAR 90%

Indian Institute of Technology,Roorkee

AKHIL ANIL KUSHE 90%

GOA COLLEGE OF ENGINEERING

AKSHITH SRIRAM ENADULA 90%

Indian Institute of Technology Madras

DOYEL SARKAR 90%

UNIVERSITY OF ENGINEERING & MANAGEMENT (UEM)

Enrollment Statistics

Total Enrollment: 7205

Registration Statistics

Total Registration : 285

Assignment Statistics



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.