FDP on Data Sciences and Machine Learning
7th - 12th November, 2016
One Week Faculty Development Program on “Data Sciences & Machine Learning" is organized by BVCOE, Delhi from 7th - 12th November, 2016. Ms. Meenu Gupta Assistant Professor from Computer Science and Engineering Department ofDronacharya College of Engineering, Gurgaon participated in the Faculty Development Program(FDP).
The objective of the FDP was to enhance research area in machine learning. The outline of the FDP was to cover Python language, data mining, Text analytics, Graph analytics, Statistical tool using SPSS, Regression, Training, Prediction Modeling, Clustering and testing. Data Sciences and Machine Learning is one of the fastest growing research areas in Computer Science with wide range of Applications
Prof. Dharmender Saini, Principal BVCOE, Delhi, inaugurated the FDP. He introduced the delegates of the event, Mr. Pulkit (3 ST Tech. Pvt. Ltd.) and Dr. Niladri Chattarjee, Associate Professor (Department of Mathematics, IIT Delhi). He explained the quality of research paper, their impact factor and the purpose of organizing the FDP.
Prof. (Dr.) Narina Thakur, HOD CSE and CONVENER also shared her views regarding the today’s research requirement and purpose of FDP on Machine Learning.
After inaugural session, Mr. Pulkit described Machine Learning and its implementation on Python Language. Participants had a hands-on session on python using regression technique and learnt how to train data set and apply testing on that data. In the afternoon, Dr. Niladri Chatterjee, explained Analysis of Text Data for Automated Learning and described all the term with the help of live examples.
Second day session was taken by Mr. Pulkit. In forenoon session, he explained Clustering techniques in Data Mining. He described clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. In the afternoon, Mr. Pulkit gave hands on session on real world data set with module numpy, Matplotlib and scikit learn. The session was very interactive and interesting.
Third day session was conducted by Dr. Sharanjit Kaur, Associate Professor, Delhi University. In forenoon session, she explained Graph Anaytics - I and conducted a hands - on session on Graph Analytics using R tool (igraph, graph, sna, foreign package), Ms. Alka Khurana assisted her during the session. In the afternoon, Dr. Sharanjit Kaur, held the session on Graph Anaytics - II and Ms. Rakhi Saxena assisted her in hands on session on community Detection in networks using R - tool.
Dr. Vasudha Bhatnagar, Associate Professor, University of Delhi conducted the fourth day session. In forenoon session, she explained Text Analytics - I and conducted a hands - on session on Text Analytics using R tool and Ms. Swagata Duraiassisted her during this session. In the afternoon, Dr. Vasudha Bhatnagar held a session on Text Analytics - II and Ms. Swagata Durai, research scholar assisted in hands on session on Text analytic using R - tool. (tm, NLP, Open NLP).
Fifth day session was conducted by Mr. Pulkit. In forenoon session, he described Predictive Modeling & Industry Level Usage and after that hands on session on Predictive Modeling Techniques using R tool is done. In the afternoon session, Mr. Pulkitexplained Markov Model and its usage Participants also had hands on session Decision Tree and Random Forest using R tool.
Last day session was conducted by Dr. Shivani Bali, Associate Professor, LBSIM, Delhi. In forenoon session, she explained Predictive Modeling: A case study on Health Care studies. After that participants had hands on session with real life data using SPSS software.
During the valedictory session, Prof. Dharmender Saini, Principal BVCOE awarded the participants with Certificates of Participation. He reiterated that this FDP enables the participants to develop the competence in understanding recent advances in Data Sciences and Machine Learning along with the case studies.