Dive into a comprehensive lecture on expression analysis, clustering, and classification in machine learning and computational biology. Explore fundamental concepts like machine learning, Bayesian inference, and various clustering techniques. Understand the distinctions between AI, ML, representation learning, and generative AI. Learn about practical applications in gene expression analysis, including K-means clustering, Gaussian mixture model sampling, and hierarchical clustering. Discover methods for clustering documents and free-form text, and gain insights into Naive Bayes classification. This in-depth presentation covers essential topics in computational biology and machine learning, providing a solid foundation for further study and application in the field.
Expression Analysis, Clustering, and Classification in Machine Learning - Lecture 2
Overview
Syllabus
Intro
What is Machine Learning
Making Inferences about the World
Reversing the Arrows: Bayesian Inference
Clustering and Classification
AI vs. ML vs. Representation Learning vs. Generative AI
ML for Gene Expression Analysis
K-means Clustering
Gaussian Mixture Model Sampling
Hierarchical Clustering
Clustering of Documents and Free-Form Text
Naive Bayes Classification
Summary
Taught by
Manolis Kellis
