DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

ENGINEERING DEGREE DIVISION

One-week Faculty Development Programme (FDP)

sponsored by

TEQIP-III PROJECT OF DR. A.P.J. ABDUL KALAM TECHNICAL UNIVERSITY, LUCKNOW

On

Advancement in Machine Learning for Business Intelligence:

Concepts, Techniques and Applications

February 26 – March 02, 2019

Email: qip@iert.ac.in

BACKGROUND

Machine learning is a new area of research. Machine Learning has been introduced with the objective to achieve original goals of Artificial Intelligence. Machine learning is one of today's most rapidly growing technologies, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in Machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. Business Intelligence (BI) technologies offer not only historical and current information but also predictive views of business operations. Machine learning fundamental core of business intelligence. In the absence of machine learning, many businesses may be unable to effectively perform market analyses, compare customer feedback, retain extremely valuable customers and arrive at intelligence business decisions. So this domains has great research potential. This course includes both theoretical lectures and hands on practice sessions on topics related to machine learning to teach and do research in this emerging field of study.

COURSE CONTENTS

The topics that are going to be covered are as follows:

1. Supervised and Unsupervised Learning

2. Statistical Learning

3. Optimization for Training Models

4. Learning with Neural Networks

5. Decision tree Learning

6. Practical Methodology

7. Machine Learning Research in Business Intelligence

8. Applications areas of Machine Learning in BI

COURSE MATERIAL

Each registered participant will be provided with a set of comprehensive lecture notes in form of soft copies/hard copies.