This course is an introduction to statistical learning using R. In this course, students are exposed to a collection of relatively simple statistical models with varying degrees of complexity. The emphasis is on the hands-on application of machine learning methods on various datasets rather than a theoretical treatment. This is a standard 1 semester course with a lab component. Previous experience with a statistical programming language is recommended but not required. Likewise, previous knowledge in college-level calculus, linear algegra, and statistics is recommended but not required. The core structure of the course is as follows:

  1. An Introduction to R
    • Data Types
    • Data Structures
    • Functions, Packages
    • Control Structures, Debugging
    • Plotting
  2. Regression
    • k Nearest Neighours Regression
    • Regression Trees
    • Gradient Descent
    • Linear Regression
  3. Classification
    • k Nearest Neighours Classification
    • Classification Trees
    • Logistic Regression
    • Discriminant Analysis
    • Support Vector Machines
    • Neural Networks
  4. Model Evaluation and Selection
    • Evaluation, Confusion Matrix, and the ROC curve
    • Cross-validation
    • Feature Selection
Just a friendly reminder:

Each day, 80k acres of forests are disappearing ...

So think about that when you try to print something next time.
      
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