Online Programme on Big Data and AI Techniques

This is a no-coding but GUI oriented and at the same time totally hands-on Program on Big Data & AI Techniques. It is an online, live, and totally interactive project-oriented program with the primary objective of disseminating techniques of Big Data Analytics to enable you to apply them on data in your research work or for teaching.

Program Contents:

Briefly, program contents are as below:

1. Introduction to Big Data Technology
2. Data visualization (including t-sne, parallel coordinates, mosaic plots) and Feature importance
3. Unsupervised learning techniques
   • Kmeans clustering
   • Hierarchical clustering
   • DBScan algorithm
   • Expectation-Maximization algorithm
   • T-SNE manifold learning technique
4. Dimensionality reduction
   • Principal Component Analysis
   • Random Projections
5. Supervised learning techniques for Classification and Regression
   • Decision trees
   • Ensemble modeling using Random Forest
   • Gradient Boosting Techniques
     • Adaboost
     • Gradient Boosting Learner
     • XGBoost
   • Handling imbalanced data—SMOTE and ADASYN
   • Performance measures: Accuracy, Precision and Recall, F-measure; Area Under the Curve, Cohen’s Kappa, Sensitivity, Specificity
6. Deep Learning Techniques
   • Neural Networks
   • Deep Learning models using H2O
7. Anomaly detection
   • Anomaly detection using isolation forests
   • Autoencoders
8. Automated Machine Learning using H2O

Pedagogy:

We strongly believe that a course in data analytics can only be practice-based rather than theory-based. We also believe that a practice-based course requires constant interaction with the teacher during lecture hours in real-time. As it is an online course, the teaching pedagogy is like this: First the algorithm (or theory part) is conceptually explained without getting into mathematics and then a project is undertaken to implement the techniques. Datasets for implementation are made available in advance. During the lecture, we create a workflow on KNIME and explain the steps. At his end, the student goes through the same steps on his laptop. Consequently, results are available at our end as also with the Students immediately. In short, both the teacher and students are working on their respective laptops simultaneously; students solve their problems and ask any questions to clarify. The whole experience is just as if everyone is sitting in a laboratory and working together. Students are required to have a laptop with a minimum of 4GB of RAM. Students will be given practical exercises (along with data) on every topic and they are expected to complete them within the time prescribed.

Program Timings and Duration:

The total program duration is 40-hours. The program will be delivered on weekdays from 2 PM to 5 PM. Thus there are 6 hours of teaching per week. During the week students are expected to perform exercises. And as the total program duration is 40 hours, it is a 6 weeks program.

Program fees:

The program carries no fees for the 20 selected students, but if a Student absents himself from more than two classes or if he does not perform Exercises to the satisfaction of the Faculty, he will be barred from further attendance.