Date: 9-12/12/2024
Time: 9:00 AM to 3:00 PM
Venue: Electrical Seminar Hall, Ground Floor, LDRP-ITR
Participants: 70 Participants
Short-Term Training Program (STTP) on Mathematics-Driven Machine Learning: Concepts, Techniques, Applications
Date: 9-12/12/2024
Venue: Electrical Seminar Hall, Ground Floor, LDRP-ITR
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About the Speaker:
Dr. Manoj Sahni
- Pandit Deendayal Energy University
Dr. Manoj Sahni is a dedicated and experienced mathematics teacher and researcher with more than 19 years of experience and currently serving as a Professor in the Department of Mathematics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India. He has an excellent academic background with an M.Sc. (Mathematics with specialization in Computer Applications) from Dayalbagh Educational Institute (Deemed University), Agra, an M. Phil. from I.I.T. Roorkee, and a Ph.D. degree in Mathematics from Jaypee Institute of Information Technology (Deemed University), Noida, India. He has published more than 100 research papers in peer-reviewed journals
Dr. Pratik Barot
- Government Engineering College, Sector 28
Dr. Pratik Barot is an Assistant Professor at the Government Engineering College, Gandhinagar. With over ten years of professional experience, Dr. Barot has worked in both academia and the software industry. His expertise encompasses data mining, computer algorithms, database systems, and Oracle
Dr. Hiten Kanani
- Government Science College, Gariyadhar
Dr. Kanani has amassed extensive teaching experience at both secondary and college levels, specializing in calculus, linear algebra, real analysis, complex analysis, and more. Currently, he teaches mathematics to B.Sc. students, preparing them for competitive exams like IIT-JAM. His expertise has also been shared through lectures and research presentations at various seminars, workshops, and conferences.
Dr. Parita Shah
- Vidush Somany Institute of Technology and Research
Dr. Parita Shah is working as an assistant professor in computer engineering at Vidush Somany Institute of Technology and Research with more than 10 years of experience.
Dr. Krunal Kachhia
- Charotar University of Science & Technology
Dr. Krunal B. Kachhia is the Head of the Department of Mathematical Sciences at the P. D. Patel Institute of Applied Sciences, Charotar University of Science and Technology (CHARUSAT), located in Changa, Anand, Gujarat, India.
Dr. Mrugendrasinh Rahevar
- Charotar University of Science & Technology
Dr. Mrugendrasinh Rahevar holds a PhD and serves as an Assistant Professor at the U & P U Patel Department of Computer Engineering, Charotar University of Science and Technology (CHARUSAT), Gujarat, India.
Dr. Brajesh Kumar Jha
- Pandit Deendayal Energy University
Dr. Brajesh Kumar Jha is working as an Associate Professor in the Department of Mathematics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India.
Dr. Safvan Vahora
- Government Engineering College, Modasa
Prof. Vahora’s research is at the intersection of Computer Vision, Machine Learning, Deep Learning, Medical Imaging and Image Processing.
Dr. Tathagata Bandyopadhyay
- DAIICT
Dr. Tathagata Bandyopadhyay earned his PhD, MSc, and BSc degrees in Statistics from the University of Calcutta. Before joining DA-IICT, he held the position of Dean (Faculty) and Professor at the Indian Institute of Management Ahmedabad.
Dr. Ojas Satbhai
- Pandit Deendayal Energy University
Dr Ojas Satbhai was awarded with Teachers Associateship for Research Excellence (TARE) by the Science and Engineering Research Board, Government of India.
9-12/12/2024At: Electrical Seminar Hall, Ground Floor, LDRP-ITR
About the Event :
Day 1 (December 9, 2024)The STTP on Mathematics-Driven Machine Learning was inaugurated with dignitaries lighting the ceremonial lamp. The day featured sessions on the mathematics of Support Vector Machines (SVM) and kernel tricks, empowering participants with classification and regression techniques through practical examples.
Day 2 (December 10, 2024)Sessions explored vector spaces, eigenvalues, and Singular Value Decomposition (SVD), emphasizing their applications in dimensionality reduction and data science. Practical demonstrations connected linear algebra concepts to modern ML algorithms.
Day 3 (December 11, 2024)Gradient Descent algorithms and their variants were highlighted, focusing on their role in optimizing ML models. Experts provided hands-on guidance on implementing these techniques for neural networks and large-scale datasets.
Day 4 (December 12, 2024)Participants delved into probability and statistics in ML, covering Bayes’ Theorem, hypothesis testing, and statistical models. Real-world applications demonstrated how these concepts drive prediction and decision-making.
Day 5 (December 13, 2024)The program concluded with advanced topics like data-driven modeling for thermal and manufacturing systems. The closing ceremony celebrated achievements, with certificates distributed and inspiring feedback shared by participants and organizers.
Glimpse of the Event :
