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Key Research Areas of FOREML Lab:
1. Time Series Forecasting 2. Imbalanced Pattern Classification 3. Neural Networks and Deep Learning 4. Spatial Modeling and Dynamical Systems 5. Health Data Sciences Statistical Learning:
I. Mathematics for Machine Learning Book: Read Online II. An Introduction to Statistical Learning: Read Online III. The Elements of Statistical Learning: Read Online Deep Learning: I. Deep Learning: Read Online II. Dive into Deep Learning: Read Online Time Series Forecasting: I. Time Series Analysis and Its Applications: Read Online II. Forecasting: Principles and Practice: Read Online |
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Guidelines for Prospective Students:
Important Reading: Below I have given some guidelines for the B.Tech/M.Sc/M.Tech students applying for MS Thesis/PhD Thesis under my supervision.
Please check whether you have done the followings before sending your application:
1. If you are a prospective student, I assume that you have a good understanding of Mathematics, Probability, and Statistics (at least up to Bachelor's level), for example, you MUST have studied the topics given in "Key Research Areas of FOREML Lab".
2. In addition to that, I would expect you MUST have studied (detailed reading and practicing all the codes by yourself in R or Python) the above-mentioned books (standard texts) on Statistical Learning, Forecasting, and Deep Learning.
3. It is desirable that you have read AT LEAST ONE of my research papers before applying.
4. Knowing at least one of the programming tools such as RStudio, Python or Matlab is essential. Knowledge of LaTeX is not mandatory but could be a plus.
5. Send your nicely prepared academic CV and transcripts to [email protected]. if you have a publication in a reputed journal or conference (not at all an essential requirement), you will be given an advantage.