Notre Dame CSE 40647/60647 Spring 2018 - Data Science

Week# Date (T/R) Lecture# Topic
1 01-16 (T) 1 Introduction
1 01-18 (R) 2 Data preprocessing: Data description (HW1 out)
2 01-23 (T) 3 Data preprocessing: Data visualization (Last date for class change)
2 01-25 (R) 4 Data preprocessing: Data cleaning and data integration
3 01-30 (T) 5 Data preprocessing: Data reduction and dimension reduction
3 02-01 (R) 6 Classification: Concepts and decision trees model
4 02-05 (M) - Project: Proposal paper due
4 02-06 (T) 7 Project: Teaming and proposal (HW1 due and HW2 out)
4 02-08 (R) 8 Classification: Naive Bayes model and Bayesian networks
5 02-13 (T) 9 Classification: Evaluation
5 02-15 (R) 10 Classification: Ensembled methods
6 02-20 (T) 11 Classification: Support Vector Machines (HW2 due)
6 02-22 (R) 12 Classification: Artificial neural networks
7 02-27 (T) 13 Course review 1 and HW1/HW2 feedback
7 03-01 (R) - Mid-term exam
8 03-06 (T) 14 Exam feedback and project QA
8 03-07 (W) - Project: Milestone paper due
8 03-08 (R) 15 Project: Milestone presentations
10 03-20 (T) 16 Clustering: Concepts (HW3 out)
10 03-22 (R) 17 Clustering: Partitioning methods
11 03-27 (T) 18 Clustering: Hierarchical, density-based, and kernel-based clustering
11 03-29 (R) 19 Clustering: Evaluation
12 04-03 (T) 20 Data Science talk 1 (HW3 due and HW4 out)
12 04-05 (R) 21 Frequent pattern mining: Concepts and Apriori
13 04-10 (T) 22 Frequent pattern mining: FP-Growth
13 04-12 (R) 23 Frequent pattern mining: Evaluation
14 04-17 (T) 24 Frequent pattern mining: Beyond itemsets
14 04-19 (R) 25 Data Science talk 2 (HW4 due)
15 04-24 (T) 25 Course review 2 and HW3/HW4 feedback
15 04-26 (R) 26 Project: Oral presentations and QA
16 05-01 (T) 27 Project: Poster presentations
16 05-03 (R) - Project: Term paper due
17 05-08 - Final exam (10:30AM - 12:30PM)