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 : Jul-Oct 2021
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 Syllabus
 Enrollment : 20-May-2021 to 02-Aug-2021
 Exam registration : 17-Jun-2021 to 17-Sep-2021
 Exam Date : 24-Oct-2021

Enrolled

5022

Registered

413

Certificate Eligible

181

Certified Category Count

Gold

6

Silver

21

Elite

55

Successfully completed

99

Participation

138

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
    Note:We have taken best assignment score from both July 2020 and July 2021 courses
Essential Mathematics for Machine Learning - Toppers list
Top 1 % of Certified Candidates

RANIT KUMAR DEY 96%

INDIAN INSTITUTE OF ENGINEERING SCIENCE AND TECHNOLOGY, SHIBPUR

PRADYUMNA PRADHAN 96%

INDIAN INSTITUTE OF PETROLEUM AND ENERGY, VISAKHAPATNAM


Top 2 % of Certified Candidates

RAGHUNATH RAM 93%

TLG India Pvt. Ltd.

G HEMANTH KIRAN VENKATA SAI RAM 92%

VISVESVARAYA NATIONAL INSTITUTE OF TECHNOLOGY


Top 5 % of Certified Candidates

DR JATIN MAJITHIA 90%

COLLEGE OF ENGINEERING PUNE

KANCHAN KUSHWAHA 90%

SARDAR VALLABHBHAI NATIONAL INSTITUTE OF TECHNOLOGY, SURAT

YASHASWI LAKKU 88%

VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING &TECHNOLOGY

DR KALA R NAYAK 88%

DON BOSCO COLLEGE OF ENGINEERING

SANTANU PODDAR 88%

Enrollment Statistics

Total Enrollment: 5022

Registration Statistics

Total Registration : 413

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.

I am enjoying the course Essential mathematics for machine learning. Live session is very useful for preperation before the exam.Thank you so much all team members.