/machine-learning

CS675 projects and files

Primary LanguageJupyter NotebookMIT LicenseMIT

Machine Learning

Learning Objectives

  1. Each week consisted of an overview and a task schedule along with specific modules that delved deep into specific concepts.

  2. The course curriculum progressed from basic notions such as k-Nearest Neighbors to more advanced methodologies such as Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

  3. This systematic approach provided students with a well-rounded understanding of machine learning algorithms and techniques.

Machine Learning Methods

  • Bayesian Classification
  • Perceptron
  • Neural Networks
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees
  • Random Forests
  • Boosting
  • Dimensionality Reduction
  • Unsupervised Learning
  • Regression
  • Generation of New Feature Spaces