CISC 4631 L01 Data Mining Class Schedule
Dr. Weiss, Fall 2024
Classes are Monday, Thursday 2:30 - 3:45, LL-311

This schedule will be adjusted based on the actual pace of the course.

Project: Guidelines   Word Templates (IEEE, ACM)    Examples (Imbalance, Finance, Biometrics, Covid Tweets)
WEKA:  Official Page, Class Page   Python:  Class Python Resource List.
Datasets: UCI ML Repository, KDD Cup, Kaggle, KDnuggets, WISDM, Cybersecurity, Kaggle Sleep Competition

• Lecture slides and homework assignments available only via blackboard. Submit homework and projects electronically via blackboard.
• Readings should be completed by the start of the associated class. All chapter readings from Intro to Data Mining.

DATE	TOPIC	READING	HOMEWORK
8/29 (R)	Review Syllabus Lecture: Intro to Data Mining I	Ch 1
9/4 (W)	Lecture: Intro to Data Mining II
9/5 (R)	Lecture: Data I	Ch 2.1-2.3 (skip sup. discr. 66-68)
9/9 (M)	Lecture: Data II	Ch 2.4 (stop at 2.4.6 p.88)	Start HW1 (due 9/19)
9/12 (R)	Lecture: Data III
9/16 (M)	Lecture: Decision Tree I	Ch 3.1-3.3 (stop at 3.3.3)
9/19 (R)	Lecture: Decision Tree II	Ch 3.3 (3.3.3 - end)	Start HW2 (due 10/2)
9/23 (M)	Lecture: Decision Tree III	Ch 3.4
9/26 (R)	Lecture: Decision Tree IV Review HW1	Ch 3.5-3.8	Download WEKA 3.8
9/30 (M)	Lecture: Decision Tree V Lecture: Class Imbalance I Discuss Course Project	Ch 4.11	Start Project Proposal (due 10/24)
10/3 (R)	Lecture: WEKA Tutorial (bring laptop loaded with WEKA)
10/7 (M)	Review HW2 Class Imbalance II		Start HW3 (due 10/21)
10/10 (R)	Midterm Review   Questions: pdf, docx   Solutions: pdf
10/17 (R)	Midterm Exam (includes all covered topics) 11am: Extra TA office hour via zoom
10/21 (M)	Go Over Midterm Exam
10/24 (R)	Sick day
10/28 (M)	Lecture: Text Classification		Project Proposal Due
10/31 (R)	Discuss Proposal Feedback Lecture: Clustering I	Ch 7
11/4 (M)	Lecture: Clustering II
11/7 (R)	Lecture: Naive Bayes Classifier Review HW3	Ch 4.4
11/11 (M)	Lecture: Association Analysis I	Ch 5.1,5.2,5.3	Start HW4 (due 11/21)
11/14 (R)	Lecture: Association Analysis II
11/18 (M)	Lecture: Neural Networks	Ch 4.7 (249-257, 261)
11/21 (R)	Lecture: Ensembles	Ch 4.10
11/25 (M)	Lecture: Nearest Neighbor Classifier Lecture: Graph Mining	Ch 4.3
12/2 (M)	TBD
12/5 (R)	Review HW4 Final Exam Review (practice final 1, solutions 1) (practice final 2, solutions 2)
12/9 (M)	Project Presentations: Christina Ou, Fiona Dark/Madison Ray		Project Paper Due 12/10
12/16 (M)	Cumulative Final 1:30 - 3:30pm