[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

• Trimino recognition (segmentation & classification)
• Human hand labeling & clustering

[MMF, spring 2021] Practicum Pt.2

• Recommendation systems: collaborative filtering. MapReduce, Hadoop

[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...