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Issue tracking system

An interface used to create, update, and resolve issues reported for the organization
Manage history and generate reports of the issues based on different criteria.

Actors :

Admin , Employee of organization including developer, QA, manager, support staff or any permissioned user.

Functionality:

Support User/ employee should be able to sign up into the system.
Admin Dashboard to see all the signed up users\

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In Class Competition Board

competitive programming platform where users can post a coding competition or participate in posted the competitions.

Actors :

Admin, Student, faculty

Functionality:

Student should be able to signup into the system.
Faculty should be able to signup into the system.
Admin Dashboard to see all the signed up users
Admin user should be able permission students to participate and admin to post the competition\

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Improvising diversity of personalized recommendation systems

Recent Research papers:

• Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques:

  107 Citations : IEEE Transactions on Knowledge and Data Engineering
  we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms

• Recommendation Diversification Using Explanations: (Data Engineering, 2009. ICDE '09. IEEE 25th International Conference)

Traditionally, the problem is addressed through attribute-based diversification grouping items in the result set that share many common attributes (e.g., genre for movies) and selecting only a limited number of items from each group. It is, however,

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Topics that deal with bias and fairness in machine learning

Fairness in Machine Learning NIPS 2017 Tutorial — http://mrtz.org/nips17/#/

Bias and Fairness in Machine Learning https://abhishek-tiwari.com/bias-and-fairness-in-machine-learning/

Attacking discrimination with smarter machine learning https://research.google.com/bigpicture/attacking-discrimination-in-ml/

Human Decisions and Machine Predictions. Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan. Quarterly Journal of Economics, 2018. https://academic.oup.com/qje/article/doi/10.1093/qje/qjx032/4095198/Human-Decisions-and-Machine-Predictions#

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Basics

Algorithmic complexity
Data Structures
* Array
* List
* Stack
* Queue
* Dictionary
* Hash Table
* Set \