SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
RAMAPURAM CAMPUS
COLLEGE OF SCIENCE AND HUMANITIES
DEPARTMENT OF COMPUTER SCIENCE AND APPLICATIONS
Two Weeks Online International FDP
A Era of Change: Recent Advancements of Machine Learning in terms of AI & IoT.
12.7.2021 to 25.07.2021
The Department of Computer Science & Applications is Organizing a Two Weeks Online International Faculty Development Program
DAY#3 -14.07.2021
CHIEF GUEST
Prof S Sundar, DAAD RESEARCH AMBASSADOR, DEPARTMENT OF MATHEMATICS, INDIAN INSTITUTE OF TECHNOLOGY, CHENNAI
TOPIC: MATHEMATICS OF MACHINE LEARNING
Total Number of Participants: 289
WELCOME ADDRESS DR.J. DHILIPAN HOD-MCA AND VICE PRINCIPAL-ADMIN
DR.J. DHILIPAN VICE PRINCIPAL ADMIN and HOD MCA presented the welcome address. During his address he welcomed the chief guest of todays function and expressed his gratitude for accepting our invitation
He concluded his address by stating that the topic chosen today will help the faculty members and research Scholars to carry out their research and application of mathematical principles related to machine learning. This is used to solve many use cases
MR.D.RAJKUMAR, Assistant Professor/MCA read the chief guest profile and introduced the guest to the audience
DAY #3- TECHNICAL SESSION
Prof S Sundar, DAAD RESEARCH AMBASSADOR, DEPARTMENT OF MATHEMATICS, IIT, CHENNAI
Prof S Sundar started the session with an explanation of Machine Learning
Machine Learning is spawning in every field. We have enormous data available and it should be processed. Example, predicting traffic in Mount Road
How do you use algorithms to predict the results?
Machine Learning is important because the algorithms that are available are playing a major role in research
He explained the importance of following mathematical foundation one should know compulsively for doing Machine Learning projects
Linear Algebra
Vector Calculus
probability and distribution
Optimization
Data and models
Linear regression
SLIQ technique
He then discussed about
Importance of mathematics in machine learning
The foundation of Machine Learning
Linear independence with example
Trace of the matrix and characteristics polynomial
Eigen values and eigenvalues vectors
Usage of page ranking algorithm by Google
Eigen decomposition – In linear algebra, eigen decomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors
He then explained about the following topics
Co variance
Basic optimization problems
Data and models with example data
Estimation of salary is explained as example
Linear regression with an example
Dimensionality reduction
SLIQ technique with an example
He concluded his address by explaining about
Decision tree with example
Tree building algorithm
Splitting algorithm
Stat log benchmark dataset
Then the entire session was opened to the audience for clarification of their doubts and interaction. The entire session was much useful for all the participants
Mrs.D. Kanchana- Assistant Professor/MCA presented the vote of thanks for the day 3 technical session
