- Lecture (Fri 12pm)
- Tutorial T*1 (Fri 11am)
- Tutorial T*2 (Fri 2pm)
- Tutorial T*3 (Fri 6pm)
- Tutorial T*4 (Tue 11pm)
- Overview (Slides)
- TBQ: What is data mining?
- Tutorial 1
- Tutorial 2
- Corona Virus
- Evaluation: The Problem of Overfitting (Slides)(Demo1)(Demo2)(Lecture 2 video)
- TBQ: How to evaluate the performance of a learning algorithm?
- Tutorial 3
- Classification: Learning from Neighbors (Slides)
- TBQ: How to lazily learn well?
- Project 1
- Tutorial 4
- Classification: Decision Tree Induction (Slides(Demo))
- TBQ: What is the best question that leads to the most informative answer?
- Tutorial 5
- Classification: Rule-Based Classification (Slides)
- Classification: Ensemble Methods (Slides)
- TBQ: How to build a strong classifier out of weak classifiers?
- Tutorial 7
- Quiz
- Classification: Different Evaluation Metrics (Slides)
- TBQ: Is it good enough for a classifier to be accurate?
- Portable test for COVID19
- Tutorial 9
- Cluster Analysis: Partitioning Methods (Slides)
Cluster Analysis: Hierarchical Methods (Slides)- TBQ: How to group similar things together while separating dissimilar things apart?
- Tutorial 10
- Cluster Analysis: Different Evaluation Metrics (Slides)
- TBQ: How to evaluate a clustering solution with/without ground truth?
- Tutorial 11
- Frequent Pattern Analysis: Apriori Algorithm (Slides)
- TBQ: How to obtain frequent patterns efficiently and turn them into rules of thumb?
- Date: May 2 (Sat) or May 3 (Sun)
- Location: Online
- Tentative Session Assignments:
- May 2 (Sat)
- Morning (9am-12:30pm): Group 1-10
- Afternoon (2pm-5:30pm): Group 11-20
- May 3 (Sun)
- Morning (9am-12:30pm): Group 21-29
- Afternoon (2pm-5:30pm): Group 30-33, 35-40
- May 2 (Sat)