Applied Machine Learning · Statistics
A lot! I do not come from a statistics background so I learnt a lot from computing to random forests to clustering.
The theoretical mechanics of statistical machine learning.
Regressions, Clustering, Random Forests, Mining
I learned more advanced machine learning techniques, including clustering, principal component analysis, decisions trees, random forest, and many others
I learned about the various algorithms which are present in the backend of many machine learning processes, such as random forests, association analysis, and preprocessing techniques.
I learned a lot on R programing and machine learning algorithm
The primary focus of this course was machine learning, specifically the many kinds of algorithms that encompass the vast field of machine learning, including but not limited to: kMeans Clustering, kNN, general regularization (LASSO, Ridge Regression), LDA, QDA, PCA, Decision Trees and their variations, and so on and so forth.
Homeworks and projects focused on data mining and machine learning methods.
Machine learning models and coding in R.
Learned relevant skills for machine learning occupations