[MMF, spring 2021] Machine Learning Pt.2
- PCA, kernel approximation: Random Fourier Features
- EM algorithm: GLAD and word alignment
- Unsupervised learning (clustering). Semi-supervised learning. KMeans, DBSCAN, Spectral, LDA, t-SNE. Geospatial (bus stops) and text (news articles) data.
- Metric learning. Imbalanced data (under / over sampling).
[MMF, spring 2021] Image Processing
[MMF, spring 2021] Practicum Pt.2
[MMF, fall 2020] Practicum Pt.1
- Python tasks
- Project 1. k-nearest neighbors
- Project 2. Logistic regression, (stochastic) gradient descent
- Project 3. Ensemble learning, web server
[MMF, fall 2020] Machine Learning Pt.1
- Introduction to pandas & matplotlib
- k-nearest neighbors with custom distance on categorical features (hacking sklearn), encoding text and categorical features
- Matrix calculus
- Linear regression, logistic regression, linear SVM, regularization, confidence calibration, feature selection, vowpal wabbit
- Bias—variance decomposition, gradient boosting, and memes...