Machine Learning in Computational Biology (Spring 2020) University of California, Berkeley
A series of labs that conduct fundamental machine learning applications utilized in computational biology and bioinformatics.
Lab 2: Writes a verison of the Expectation Maximization algorithm to conduct DNA motif finding.
Lab 3: Determines the practical distributions of the basis for a dataset of RNA sequences in various conditions.
Lab 4: Uses a dataset of gene expression for 20 samples and performs k-means clustering and PCA.
Lab 5: Implements a linear regressor using gradient descent upon a dataset of RNA sequences to update the parameters and determine the optimal solution.
Lab 6: Classifies cell images from thin blood smear slides of segmented cells, with labels indicating the presence of malaria using Keras.
Lab 7: Analyzes gene expression of 20 samples, determining patterns in the dataset using principal component analysis, k-means implementation, and t-SNE for dimensionality reduction and visualization