Genomic Data Analysis: Techniques and Applications

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Robust Regression in Genomic Data Analysis

Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Clustering: Unsupervised Learning

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

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Regulation of Transcription and Translation

Explores the regulation of transcription and translation processes, focusing on control mechanisms and alternative splicing in eukaryotes.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.