The Section Distinguished Lecture Program was conducted by IEEE IAS Student Branch Chapter NSS College of Engineering, Palakkad in collaboration with IEEE Kerala Section and IA/IE/PELS Jt.chapter Kerala on 31st October 2019. The half-day session was handled by renowned speaker Dr. Deepak Mishra, Department of Avionics, IIST Thiruvananthapuram. The Inaugural function started with a prayer. IEEE Code Of Ethics is conveyed by Abhinav Rajeev, Chairman, IEEE SB NSSCE. Welcome address Associate Professor Dr. Vasanthi V Advisor, IAS SBC NSSCE. Presidential address by Principal, NSS College Of Engineering Palakkad.
The program officially inaugurated by Dr. Deepak Mishra. Felicitation by Assistant Professor Vijitha S Branch Councilor, IEEE SB NSSCE.
Finally, the inaugural function ended with Vote of Thanks Ajith M, Chairman, IAS SBC NSSCE. The distinguished lecture program was on Machine Learning, which is very prominent as far as technical education is concerned. About 65 students attended the session. Among them, there were IEEE IAS student members of NSS Collge of Engineering and Ammini College of Engineering. The lecture was worth attending in which topics like Neural Networks and Deep Learning were covered.
After attending the session we could analyse the scope of deep learning and neural networks in machine learning. The lecture started with deep learning, how the machine processes two similar images and distinguishes the two, such as a Barn owl and an apple.We could also understand that Mathematics, especially probability and statistics are indispensable in learning the concepts of deep learning.
As we all know, probability is the science of quantifying uncertain things.Most of machine learning and deep learning systems utilize a lot of data to learn about patterns in the data. Whenever data is utilized in a system rather than sole logic, uncertainty grows up and whenever uncertainty grows up,probability becomes relevant. Due to this fact, concepts like Bayes decision theory and Nearest Neighbour Classifier were discussed. Bayes decision theory mainly focuses on Posterior probability which says which class is more likely to happen. Bayes classifier minimises error. But in the case of Nearest Neighbour classifier, there is no statistical view point, hence the error in Nearest Neighbour is twice than that of Bayes classifier.
Another concept discussed was discriminant function. Professor also explained that neural network is an example for Non linear discriminant function. A simple iterative algorithm called Perceptron Learning algorithm which comes under linear classifier was discussed as well. It was also informed that the most basic principle in neural network was inspired from the working of the human brain and single neuron model in turn from Perceptron theory. At the end of the session all the participants could understand the important concepts of machine learning.The session ended with the feedback from the students. As token of appreciation Dr. Leesha Paul, Ex- Student Branch Councellor presented memento.