Human Centric AI | Projects

Human Centric AI Projects

S

Sunmeet Kohli

Matr. Nr: 642365

Hover to see contact info

S

Sanika Acharya

Matr. Nr: 640981

Hover to see contact info

Exploring the intersection of artificial intelligence and human-centered design to create meaningful solutions.

View Projects

Our AI Projects

A collection of our human-centric AI projects demonstrating ethical AI development and practical applications.

Project 1

Data Visualization & ML

This project creates a Django-based web application to simplify supervised machine learning tasks. Users can upload CSV datasets, explore data through visualizations, and train various machine learning models using an interactive interface. The app handles data splitting, model training, and evaluation, making it easy for users to build and assess predictive models without deep programming knowledge.

Python Scikit-learn Javascript
Project 2

Active Learning for Text Classification using IMDB Reviews

This project explores active learning methods to efficiently train a sentiment classifier on the IMDB 50k movie review dataset. Starting with a fully supervised model as a baseline, the project then simulates label-efficient learning by interactively selecting the most informative samples, aiming to achieve high accuracy with minimal labeled data.

NLP ActiveLearning IMDB
Project 3

Interactive Explainable Model on Penguin Species

This project focuses on building interpretable models using the Palmer Penguins dataset. It includes an interactive interface to explore decision trees and logistic regression with adjustable model complexity, as well as a counterfactual explanation tool to understand predictions and suggest minimal changes for alternate outcomes.

scikit-learn palmerpenguins Visualization
Project 4

Interactive Cold-Start Movie Recommender

This project addresses the cold-start problem by letting new users rate movies while seeing how their input shapes future recommendations. Using the MovieLens dataset, it builds an interactive, user-guided system to quickly learn individual tastes.

Recommender MovieLens MatrixFactorization
Project 5

Reinforcement Learning with Human Feedback: Training a Cheese-Seeking Mouse Agent

This project trains a mouse agent in a 5×5 grid using reinforcement learning. The REINFORCE algorithm helps it find cheese while avoiding traps. Human feedback adjusts rewards to discourage eating organic cheese.

RLHF BradleyTerry RewardLearning