CS 251: Lecture Notes

### Quizzes

Zip file of quizzes

### Lecture Notes

- Examples of good and bad visualizations
- Lecture 1 (introduction, Feb 5)
- Lecture 2 (good/bad data viz, Feb 7)
- Lecture 3 (Tkinter, Feb 10)
- Lectures 4-5 (coordinate systems, Feb 12-14)
- Lecture 5 (numpy, Feb 14)
- Lecture 6 (data class, Feb 17)
- Lectures 7-8 (3D viewing pipeline, Feb 19-21)
- Lectures 9-11 (3D interactive viewing, Feb 24-28)
- Lecture 12 (PDFs and histograms, Mar 3)
- Lecture 14 (statistics, range selection, Mar 7)
- Lecture 15 (PCA, Mar 10)
- Lecture 16 (PCA, Mar 12)
- Lecture 17 (noise, Mar 14)
- Lecture 18 (machine learning and data mining, Mar 17)
- Lecture 19 (distance metrics and k-means clustering, Mar 19)
- Lecture 21 (hierarchical clustering, Mar 31)
- Lecture 22 (fuzzy c-means clustering, Apr 2)
- Lecture 24 (Naive Bayes, Apr 7)
- Lecture 25-26 (Evaluating classifiers, Apr 9,11)
- Lecture 27 (Decision trees, Apr 14)
- Lecture 28 (Decision trees continued, Apr 16)
- Lecture 30 (Neural Networks, Apr 21)
- Lecture 31 (Neural Networks continued, Apr 23)
- Lecture 32 (Neural Networks continued, Apr 25)
- Lecture 33 (Linear regression, Apr 28)
- Lecture 35 (Genetic Algorithms, May 5)