Practice Problems – The Noders

Beginner

Electric Motor Temperature Estimation

RMSE (Root Mean Squared Error) · Regression

Building upon real-world automotive diagnostics and thermal monitoring benchmarks, this project leverages multi-variable sensor streams to estimate critical internal temperatures of a Permanent Magnet Synchronous Motor (PMSM). Instead of relying strictly on independent cross-sectional regression, participants will navigate sequential 2 Hz data streams, address the open challenge of missing values from cropped data segments, and generalize to entirely unseen driving cycles. By designing unified multi-output systems or ensembled sub-models under rigid offline constraints, this challenge highlights how advanced feature engineering and temporal dynamics can reliably infer economically unmeasurable physical parameters for motor safety and control optimization.

Tabular_Regression, Multi-output

119.1769Solve →

Beginner

Butterfly Image Classification

Accuracy · Classification

This project utilizes a curated Butterfly Image Dataset to build robust Computer Vision models for multi-class categorization. Participants will optimize deep learning architectures (like CNNs) to accurately predict the exact butterfly species based purely on raw image pixels.

CV_Classification, CV_Dectection

21.0000Solve →

Beginner

Large Movie Review Dataset (IMDB) with Pretrained Word Embedding Approaches

F1 Score · Classification

Building upon classic NLP classification benchmarks, this project leverages pretrained word embeddings to construct sentiment classification models on the Large Movie Review Dataset (IMDB). Instead of relying strictly on sparse frequency-based representations like Bag-of-Words or TF-IDF, participants will explore the usage of dense vector representations to capture deeper semantic relationships. By integrating fixed, pretrained word vectors with downstream classifiers or custom aggregation strategies, this challenge highlights how semantic feature transfer can improve natural language understanding under rigid computational constraints.

NLP_Classification, SpaCy, Gensim_GloVe

10.7748Solve →

Beginner

Large Movie Review Dataset (IMDB) with Machine Learning Approaches

F1 Score · Classification

Modeled after landmark NLP benchmarks and academic text-classification challenges, this project utilizes the renowned Large Movie Review Dataset (IMDB) to build machine learning models for binary sentiment classification. Participants will experience a classic natural language understanding pipeline, optimizing traditional machine learning models to accurately predict whether a movie review is positive or negative based purely on raw text.

NLP_Classification, SpaCy

10.8087Solve →