Personal Notebooks for the Foundation of Applied Machine Learning (PHYS 243) Fall 2021 course.
This course covers concepts such as General definitions, and python programming, Bayesian statistics, Basic linear algebra, Statistics, Simulations, Multivariate statistics , Statistics, Confidence intervals, Machine Learning: Regression, Clustering and Classification, Machine Learning: Regression, Gradient Descent, Decision Trees, Random Forests, Support Vector Machines, Neural networks, extras, Manifold Learning, Generative models, etc.
More info on this course can be found here: https://msol.ucr.edu/courses/phys243
Note that the final project notebook for the https://www.kaggle.com/c/tmdb-box-office-prediction/overview Kaggle competition scored higher than previous analyses to date.