A collection of university machine learning projects exploring various algorithms and techniques.
PCA, Diffusion Maps & Variational Auto-encoders
- Principal Component Analysis (PCA)
- Diffusion Maps for non-linear dimensionality reduction
- Variational Auto-encoders for generative modeling
- Pedestrian simulation analysis
Extracting Dynamical Systems from Data
- Linear and non-linear system analysis
- Vector field reconstruction
- Mutual Information analysis
- Takens embedding theorem
Regression, Classification & Clustering
- Linear regression implementation from scratch
- Logistic regression and SVM classification
- K-Means clustering algorithm
- Model limitations and failure analysis
Each project contains:
- 📓 Jupyter notebooks with implementations
- 📊 Data files and datasets
- 📄 Final reports
- 📖 Detailed README files
- Akshay Pimpalkar
- Shashwat Rishi Tiwari - Projects 4-6
- Witold Merkel - Projects 4-6