arxiv-frontpage

My personal frontpage app

bachelor-thesis-textgeneration-th.ohm

IT-Based textgeneration with the use of NLP methods. A text summarization task is conducted with the amazon fine food review dataset from Kaggle. This task is done by attention and lstm neural networks.

cifar-10-cs4487-assignment

Final project of my Course Machine Learning CS4487 as exchange student at the City University of Hongkong.

climate_challenge_hackathon

clustering-kmeans-python-is4861-assignment

Clustering Assignment of my Course Machine Learning for Business IS4681 as exchange student at the City University of Hongkong.

covid19-germany-region-case-prediction

This was my final assignment for the course Clinical Data Science at the Friedrich Alexander University Department of Health for Master of Computer Science.

data-science-interviews

Data science interview questions and answers

disaster-response-unit-figure_eight

A Machine Learning pipeline to categorize disaster events so that you can send the messages to an appropriate disaster relief agency

dql_connect4_mathematical_data_science_ss2020.fau

Final Project of the Master course Mathematical Data Science at the Friedrich Alexander University Erlangen-Nürnberg. We received the grade A for this.

gapstatistics

Computing the gap statistics from Tibshirani et. al. for various clustering algorithms

it-project-autonomous-rccar-th.ohm

University IT-Project: Autonomously driving Remote Control Car

kaggle-seattle-airbnb-analysis-is4861-assignment

Final project of my Course Machine Learning for Business IS4681 as exchange student at the City University of Hongkong.

linkedin-job-scraper-data-analysis

Jobs have been scraped from linkedin to perform a data analysis to optimize the CV and cover letter

machine_learning_in_the_industry_4.0_seminar_ss2020.fau

Identifying a Trial Population for Clinical Studies on Diabetes Drug Testing with Neural Networks

mavengence

mavengence.github.io

pattern_analysis_ss20_fau

programming-microcontrollers-elective-th.ohm

IT project for the elective course Programming Microcontrollers as undergrad student at the University of Applied Science Georg Simon OHM in Nuremberg, Germany

representation-learning-for-gait-analysis-in-parkinson-s-patients

This project aims to quantify how accurately Morbus Parkinson's can be classified by different types of deep learning architecture without preprocessing the original sensor data. For this purpose, four different architectures (LSTM, ResNet, a basic autoencoder and a ResNet autoencoder) were used to evaluate the accuracy. The data was collected from patients at the University Hospital of Erlangen. Different severity levels of Parkinson's were regarded as being deceased. In this regard, this project performed a binary classification task (healthy and deceased). It shows, that a ResNet autoencoder predicts Parkinson with 87% accuracy and can be used as a decision support system for doctors.

scikit-learn

scikit-learn: machine learning in Python

style-classification-in-posters

trend-detection-data-set_politics

Artificial Intelligence Trend Analysis in German Business and Politics - A Web Mining Approach

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.