Cluster Analysis: Metrics by Dylan Owen

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Optimization and Simulation: Bayesian Inference

Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Protein Stoichiometry: Single-Molecule Localization Microscopy

Explores automated analysis of fluorescence kinetics in microscopy data to reveal protein stoichiometry and its applications.

Expectation Maximization and Clustering

Covers the Expectation Maximization algorithm and clustering techniques, focusing on Gibbs Sampling and detailed balance.

Simulation: Control of Dynamic Systems

Explores simulation of common structures and control of dynamic systems through friction and elasticity constants.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Clustering Algorithms: K-Means vs Spectral Clustering

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.