(r1) CSC314SylS2025 < CenterForComputing

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---++ Details
   * Professor: Arisoa Randrianasolo
   * Office: Online via Zoom.
   * Office hours MWF:10-11am, 1-2pm, 3-5pm
   * Class Time: Online
   * Class Location: Online
   * Textbook: No text required.
---++ Course Content

The course will consist of lectures (online), labs and projects.
---+++ Policies
   * [[AcceptableWork][Integrity]]
   * [[LatePolicy ]]

---+++ Assignments
   * Weekly Labs contribute to 70% of your final grade
   * Midterm project 15% of your final grade
   * Final project 15% of your final grade
---+++ Grades

Your grades are made up of:

   * Grade scale
      * 93% &lt;= average &lt;= 100% -&gt; A
      * 90% &lt;= average &lt; 93% -&gt; A-
      * 87% &lt;= average &lt; 90% -&gt; B+
      * 83% &lt;= average &lt; 87% -&gt; B
      * 80% &lt;= average &lt; 83% -&gt; B-
      * 77% &lt;= average &lt; 80% -&gt; C+
      * 73% &lt;= average &lt; 77% -&gt; C
      * 70% &lt;= average &lt; 73% -&gt; C-
      * 67% &lt;= average &lt; 70% -&gt; D+
      * 63% &lt;= average &lt; 67% -&gt; D
      * 60% &lt;= average &lt; 63% -&gt; D-
      * 0% &lt;= average &lt; 60% -&gt; F

---+++ Tentative Schedule
| Week | Topics and Activities |
| 1 | Jupiter lab and python essential (identation,loop,selection, list) |
| 2 | python reading from file |
| 3 | Intro to supervised learnig: Linear regression |
| 4 | K-nearest neighbor |
| 5 | Naïve Bayes |
| 6 | Support Vector Machine |
| 7 | Support Vector Machine with Kernels |
| 8 | Midterm Project |
| 9 | Neural Network (identity) |
| 10 | Neural Network (other activation functions) & deep network |
| 11 | Intro to unsupervised and Principal Component Analysis |
| 12 | Kmeans clustering |
| 13 | Density Based clustering |
| 14 | Agglomerative clustering |
| 15-16 | Final Project |
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Topic revision: r1 - 2024-12-20 - CathyBareiss