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
• 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.
```      ```
- - -
-        -  -     --    -
-                 -         -  -
-
-                --
-          -            -              -
-            '-,        -               -
-              'b      *
-              '\$    #-                --
-    -           \$:   #:               -
--      -  --      *#  @):        -   - -
-     :@,@):   ,-**:'   -
-      -,         :@@*: --**'      -   -
'#o-    -:(@'-@*"'  -
-  -       'bq,--:,@@*'   ,*      -  -
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-     '  - '@@Pp@@*'    -  -
-  - --    Y7'.'     -  -
:@):.
.:@:'.
.::(@:.      -Sam Blumenstein-
```
```