Machine Learning Techniques’ Workshop by Department of Computer Science and Applications

Chandigarh, February 4, 2022: The Department of Computer Science and Applications, Panjab University, Chandigarh organized One Day Workshop on ‘Machine Learning Techniques’ today. The program started with the welcome address by Dr. Rohini Sharma, Chairperson and Coordinator of the workshop. She informed that around 300 participants from 15 different states of India, from industry and from overseas registered for the event. Prof. S. K. Tomar, Dean University Instructions talked about the pros and cons of technology in human life. He expressed that machine learning is an emerging area and various developments in this field will help the current and future generations.

Prof. Tankeshwar Kumar, Hon’ble Vice-Chancellor of the Central University of Haryana inaugurated the workshop. He linked the idea of machine learning with astrology and astronomy in ancient times where through observations, predictions were made. He shared the idea that machine learning has tremendous applications in healthcare, agriculture, business and many other sectors. The predictions made by machines may help us to protect our environment, do wonders in healthcare to save lives and bring huge changes in the world. The inaugural session concluded with the Vote of Thanks delivered by Prof. Indu Chhabra.

In the first technical session, Prof. Prateek Bhatia, Visiting Professor at LAMBDA Lab, at Tel Aviv University, Israel and Professor at Thapar Institute of Engineering and Technology, Patiala;, discussed about Identification of objects with Deep Learning Techniques. He explained in detail the working of Convolutional Neural Network with the description of different steps like convolution, max pooling, flattening and full connection. The session ended with an interesting quiz on the topic covered.

In the second technical session of the day, Dr. Anuj Sharma, Department of Computer Science & Applications, Panjab University, Chandigarh. (Post Doc from RWTH Aachen University, Germany) explained Machine Learning based Regression and Classification techniques like linear, polynomial, support vector, decision tree, random forest regression, logistic regression, k-nearest neighbour, naïve bayes along with implementation. The workshop was well appreciated by the participants.