Department of Technology
Machine Learning
Aditya Abhyankar

Machine Learning

A course on Machine Learning by Prof. Aditya Abhyankar offered at Department of Technology.

Computational Fluid Dynamics
SUKRATU BARVE

Computational Fluid Dynamics

The course introduces students to continuum modeling of matter and helps the student get train in applying techniques of vector and tensor calculus to the same. Fluid models are then developed as balance and constitutive laws and related partial differential equations are derived. Therefrom, basic types of related numerical methods are described (Finite Difference, Finite Volume, Finite Element Spectral and Meshfree methods) and numerical techniques of solving difference equations (Lax Wendorff and MacCormack)  are discussed. The emphasis is on Finite Difference and Finite Volume methods and related difference equations solving techniques.  Open source softwares are introduced in practical work. OpenFOAM and SU2 (depending upon student response) are taught at solver and case level. Basic ParaFOAM GUI familiarity is achieved.

CBE46_Cancer Genomics
Swapnil Kamble

CBE46_Cancer Genomics

Cancer Genomics involves understanding of multiple tools and techniques utilized in Genomics towards specific application in Cancer. Broad syllabus includes:
Human Genome project, The Structure, function and evolution of human genome, strategies for large scale sequencing, comparative Genomics
>High through put technologies in genomics, Next Gen sequencing
> Cancer Biology, Cancer Genetics, Therapy

> Application of various tools to decipher genomics of cancer


Text and Web Mining
VANDANA DHINGRA

Text and Web Mining

The Web is the largest collection of electronically accessible documents, which make the richest source of information in the world. Web Mining deals with the automatic discovery of interesting and useful patterns from the data associated with the usage(log data), content, and the linkage structure of Web resources. With the dominance of unstructured text information over the Internet, mining high-quality information from text has become increasingly critical. Extracting knowledge from the text data available on the web has largely become one of the most popular areas in computing and information systems because of its direct applications in business intelligence, e-CRM, Web analytics, information retrieval/filtering, Web personalization, Social behavior analysis, and recommender systems. This course deals with understanding algorithms for extracting knowledge from the web by applying Machine Learning, Information retrieval and text mining techniques. 

Vibration and Noise Control
BEENA LIMKAR

Vibration and Noise Control

This course will give an overview on Mechanical Vibration fundamentals and applications in real-life situations. A systematic understanding of the subject is built by using a systematic teaching approach. On completing the course you will be empowered to solve real-life vibration problems. 

 Initially, an in-depth understanding of mechanical vibration fundamentals is developed using a single degree of freedom system. Based on this knowledge two-degree and multiple-degree system theory is developed.

This course will also cover Numerical methods for vibration analysis, Modal analysis using Finite element analysis (FEA) software, Experimental modal analysis, and Vibration of continuous systems.

Noise control methods and theory will conclude the course.


Machine Learning

Machine Learning

Machine Learning is a foundation course and exposes students to various supervised and unsupervised techniques. Further the course also exposes students to advanced topics like Fuzzy Logic, Genetic Algorithms, Probabilistic Systems, Deep Learning etc.

Advanced Modelling Techniques
Mandar Badve

Advanced Modelling Techniques

In this course, we will study how various chemical/biological process are described mathematically.  We will develop and formulate mathematical models for different processes and we will study various techniques to solve mathematical equations/models using MS Excel, MATLAB.