A Modern Approach To AI : GIAN workshop
Niharika | Oct 23, 2017
In the previous issue, Team Monday Morning brought you the details of the first half of the seminar on Artificial Intelligence for Magnetic Resonance Brain Image Processing under GIAN organized by the Department of Computer Science and Engineering. Please scroll to the footnote to find the link to the previous article. The seminar began on 9th October and around 76 students of Ph.D., M.Tech and B.Tech from a multitude of institutes of India were a part of this seminar. The seminar was planned for two hours of lecture followed by two hours of tutorial every day from 9th October until 18th October. Following are the details of the last five days of the program:
The sixth day began with the enlightening lecture of Dr Yudong Zhang on advanced wavelet analysis, stationary wavelet transformation, wavelet packet transformation and dual-tree complex wavelet transformation. This was followed by the continuation of Tsallis entropy (Part-II) which started on the 4th day. Furthermore, Prof. Bansidhar Majhi gave a lecture on the Extreme Learning Machine (ELM) and Evolutionary ELM. It was succeeded by a tutorial on the usage of Matlab 2017a to implement pathological brain detection using ELM and its variants.
The 7th day commenced with the lecture of Curvelet transformation and Ripplet transformation for feature extraction by Prof. Ratnakar Dash. This was accompanied by Dr Yudong Zhang’s lecture on Support Vector Machine (SVM) and parallel versus non-parallel (part-II). Moreover, a tutorial on the usage of Matlab 2017a (Classification Learner Toolbox) to implement pathological brain detection (Part-II) was continued further.
A lecture on Artificial Neural Network (ANN), Feedback Neural Network (FNN) and Multilayer Perceptron (MLP) was imparted by Dr Yudong Zhang on the 8th day of the GIAN course. He also provided information on the training of FNN and MLP to detect pathological brains.
The ninth day witnessed an illuminating lecture by Dr Yudong Zhang on the difficulties in the deep neural network and its rectification. He also threw light upon the concept of Convolutional Neural Network, its structure, and its training methods. This was followed by a tutorial on the utilization of Matlab 2017a to design a convolutional neural network for object recognition and hearing loss brain detection.
The final day of the GIAN course was set in motion by the comprehensive lecture of Dr Yudong Zhang on Autoencoder, sparse autoencoder, Regularization in autoencoder and stacked autoencoder. This was succeeded by a tutorial on the usage of Matlab 2017a to design a stacked sparse autoencoder for hearing loss brain detection.
The entire course of events concluded on a successful note with the valedictory address by Dr Yudong Zhang. We at Team MM hope that more such events are organized by the institute to increase the knowledge as well as the exposure of the students to the modern technology.