Course Material

Week	Activity	Notes
1	Introduction to Data Mining
2	Eid Break
3	Data Exploration
4	Data Preprocessing
5	Classification (Decision Trees)-	Hijri New Year (1 day off)
6	Evaluating Classifiers
7	First Exam Period
8	Bayesian Classifiers
9	Instance-based Methods
10	Ensemble Methods
11	Clustering Techniques
12	Clustering Techniques
13	Second Exam Period
14	Advanced Topics	New Year (1 day off)
15	Advanced Topics	Mawlid Nabawi (1 day off)
16	Final Exams

Lecture Slides

- Course Introduction.
- Basics.
- Know Your Data.
- Data Preprocessing.
- Data Exploration.
- Decision Trees.
- Evaluation.
- Nearest Neighbor Classification.
- Naive Bayes.
- Model Ensembles (Other Slides).
- Clustering.

Book Chapters:

- Introduction [From the Data Mining book by Ian H. Witten, et al.]
- Know Your Data [From the Data Mining book by Han et al.]
- Data Preprocessing [An online summary].
- Classification [From the Data Mining book by Pan-Ning Tan et. al.]
- Nearest Neighbor Classification [From the Machine Learning book by Tom Mitchell]
- Bayesian Classification. [An online tutorial]
- Clustering [From the Data Mining book by Pan-Ning Tan et. al.]
- Association Mining [From the Data Mining book by Pan-Ning Tan et. al.]