CS 251: Lecture Notes

### Papers

Bruce's paper on the Australian coast data
### Lecture Notes

- Lecture 35 (Artificial Neural Networks, May 4)
- Lecture 33 (Pruning Decision trees, Apr 26)
- Lecture 30 (Decision trees, Apr 20)
- Lecture 28 (Evaluating classifiers, Apr 15)
- Lecture 27 (K Nearest Neighbors, Apr 13)
- Lecture 26 (Naive Bayes, Apr 10)
- Lecture 25 (fuzzy clustering, Apr 8)
- Lecture 24 (hierarchical clustering, Apr 6)
- Lecture 22 (k-means clustering, Apr 1)
- Lecture 21 (PCA interpretation, Mar 30)
- Lecture 18 (PCA, Mar 18)
- Lecture 16 (regression, Mar 11)
- Lecture 15 (introduction to machine learning and data mining, Mar 9)
- Lecture 14 (noise, Mar 6)
- Lecture 13 (Basic statistics aid visualization, Mar 4)
- Lecture 12 (Probability Distributions and Histograms, Mar 2)
- Lecture 9 (3D Camera Control, Feb 13)
- Lecture 7 (3D Pipeline, Feb 18)
- Lecture 6 (Numpy and Proj2 prep, Feb 16) and today's code: lecture6.py and lecture6_data.csv
- Lecture 5 (Numpy, Feb 13)
- Lecture 4 (Coordinate Systems, Feb 11)
- Lecture 3 (Tkinter, Feb 9)
- Lecture 2 (good and bad viz, Feb 6)
- Examples of good and bad visualizations
- Lecture 1 (introduction, Feb 4)