Prof. Ratnik Gandhi | Profile |
Rahul Patel | Profile |
This is a PG course (open as a technical elective to senior UG). The course will evolve around reading and implementation of state of the art literature in streaming algorithms related to big data problems. The objective of this course is to expose students with state of the art literature in the area of algorithms designing specifically for big data (focusing on streaming algorithms and related optimization problems). Student taking this course will develop an ability to independently take up a problem related to big data, model it and design a relevant solution.
This will be a Laboratory-class based course. Every week we will meet for a 3 hours session. During this session we will be discussing one or two ideas from reference research papers. Further, in this session, you (students) will be implementing these ideas in relevant software systems
|Midterm Project||30%||A 3 Week project individual project - Implementation of an existing research paper.|
|Endterm Project||30%||A 7 Week project group project - Implementation and extention of an existing research paper.|
|Endterm Take-home||40%||A one week individual assignment - Propose solution/s for the open-ended problem provided.|
Consider a scenario in which a company like LinkedIn wants to build a module for suggesting career progression paths to its registered users. When a user logs onto the platform, the platform reads user’s profile and based on various parameters of this profile comes up with relevant suggestions on how the user should consider next set of skills to be acquired. Your aim, through this exam, is to design the following two modules:
Relevant user profile data is available here. You are supposed to design modules for
Significantly unique solutions will be appreciated.
Submission will be a report (maximum 3 page for diagrams/algorithm/results and other discussions) and GitHub code. A neat and clean algorithm with relevant proofs of correctness and its efficiency (recorded as a measure of computational complexity) will fetch more grades. 
Submit your solutions to Rahul on April 28, 2017 between 10am to 11am.
List of assignments with solutions.
|1||Online Regression||Parth Satodiya: Tensorflow|
|2||Online Singular Value Decomposition||Kishan Raval|
|3||Robust Principle Component Analysis (Midterm Project)||Riddhesh Sanghavi|
|4||Probabilistic Principle Component Analysis (PPCA) using Expectation Maximization||Parth Satodiya|
|5||Incremental Principle Component Analysis||Maunil Vyas Sol.|
|6||Generative Adversarial Network using PPCA (Endterm Project)||Maunil Vyas, Deep Patel and Shreyas Patel Sol.|
|7||Online K-medians Clustering||Shreyas Patel Sol.
|8||Incremental Linear Discriminant Analysis||Shreyas Patel Sol.
|8||Endterm Take-home||Maunil Vyas and Deep Patel Sol.
Ashutosh Kakadiya Sol.
Link to the excel file containing list of student repositories.