# Projects

### The 2nd YouTube-8M Video Understanding Challenge:

- Designing a machine learning model with size constrained of 1GB to predict video labels. Experimented with three different architectures.
- Our team could achieve the
**Global Average Precision (GAP) score of 0.82853**with final**rank of 104**among the participated 394 teams across the different countries. - [code], [ Kaggle Challenge Link]

### Using Generative Adversarial Network (GAN) for Image Completion:

- Goal is to use GANs for completion of face images, important in many applications such as surveillance.
- Implemented in TensorFlow. Completed as a course project for Deep Learning for Vision course.
- Mentor: Dr. Vineeth N Balasubramanian
- Project Report

### Inverse Search:

- The goal is, given a set of documents, find the most probable query that might have generated these documents. Used ensemble of 3 algorithms:TF-IDF based, LDA based and TextRank.
- Implemented using Python NLTK, Java MALLET. Completed as a course project for Information Retrieval course.
- Mentor: Dr. Maunendra Desarkar

### Time-table Generation Using Genetic Algorithm:

- Every semester the university needs to generate the class time-table that satisfies certain hard-constraint and soft-constraints.
**Generating optimal time-table**manually is laborious and challenging task. Hence, we designed an algorithm that makes this process automatic. - We considered six factors: department, class, course, weekday, time and room. We constructed a string of integer numbers indicating factors that are allocated. This
**string indicates the chromosome**. Based on whether this allocation satisfies constraints the fitness score for this chromosome is calculated. We then use**two-point crossover and mutation**to find optimal chromosome (i.e. time-table). - This project was part of my
**B.Tech Thesis**and completed during final year of B.Tech at VIT - Mentor: Dr. Manasi Patwardhan

- Every semester the university needs to generate the class time-table that satisfies certain hard-constraint and soft-constraints.
### Exam-scheduling Using Graph Coloring Algorithm:

- In university, the courses are offered to students from different departments. Hence, while scheduling an exam for such courses, examination authorities need to take care that exam time-slot should not clash with other courses for which students have already registered.
- To solve this problem, we constructed a
**graph with courses as nodes**and edge indicates that there exist at least one student who has registered for both courses (represented by two end nodes of an edge). - This formulation has
**simplified the problem of exam scheduling to graph coloring**, where color of the node represents the time-slot of alloted for the course. As per the graph coloring algorithm, we need to assign colors to nodes such a way that no two adjacent nodes can have same colors. - This project was completed during my
**third year of B.Tech** - Mentor: Dr. Manasi Patwardhan