Webinar on "Calculus for Machine Learning"
A lecture on “Calculus for Machine Learning” to enlighten the students on the applications of calculus in machine learning is delivered by Mr V Ganesh Kumar (Assistant Professor, H&S) who is the resource person for the day.
- The speaker started the session with a review on the definition of calculus by highlighting the difference between algebra and calculus, also taking note of the branches of calculus ie. Differential and Integral calculus.
- Basic concepts of calculus such as Partial Derivatives, Chain rule, Maxima and Minima, Jacobian and Extreme values were discussed with relevant examples.
- Role of Calculus in Machine Learning was disuceesd with the help of the Gradient descent method and Loss Function.
- The implementation of calculus in python was explained with the help of a program.
- Concepts like Regression line and Least Square method were discussed to help the participants get a better understanding.
- A quiz on “Mathematics for Machine Learning” was conducted in the latter part of the session.
Date and Time
- Date: 24 Jun 2020
- Time: 02:00 PM to 04:00 PM
- All times are (GMT+05:30) Asia/Calcutta
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Mr V Ganesh Kumar of VNR Vignana Jyothi Institute of Engineering and Technology
Calculus for Machine Learning
Assistant Professor, Department of Humanities & Science.
Mr V Ganesh Kumar has a teaching experience of over 7 years. He has pursued an M.Sc. in Applied Mathematics and an M.Tech in the field of Industrial Mathematics and Scientific Computing.
The session aimed to discuss calculus in detail by answering simple questions based on the use and need of calculus. The session was planned by giving equal weightage to topics such as the Branches of Calculus, Multivariate Calculus, Role of Calculus in Machine Learning and its other Applications.
A total of 45 people attended the lecture and were able to recaptulate their knowledge of Calculus. The speaker ensured that the session was interactive throughout and helped the participants grasp, the concepts of Calculus involved in Machine Learning.
|Screenshots of the session