General Info

CPSC 599.44: Machine Learning - Timetable

The following table gives the planned schedule for the semester for all lecture dates. You find also links to the two different formats of pdf files under the materials column (if a new file was needed for the lecture).

Date Lecture Topics Materials Deadlines
Jan. 14 Introduction handout, single slide   
Jan. 16 Introduction      
Jan. 21 Rules: Introduction, Learning association rules handout, single slide   
Jan. 23 Learning association rules, Inductive Logic Programming      
Jan. 28 Inductive Logic Programming      
Jan. 30 Parameters: Introduction, Linear regression analysis handout, single slide   
Feb. 4 Linear regression analysis, Support vector machines handout, single slide   
Feb. 6 Support vector machines      
Feb. 11 Neural networks      
Feb. 13 Neural networks      
Feb. 25 Trees and Graphs: Introduction, ID3 handout, single slide   
Feb. 27 ID3, Learning Bayesian Networks handout, single slide   
Mar. 3 Learning Bayesian Networks      
Mar. 5 Partitioning of sets: Introduction, k-means clustering handout, single slide Mar. 6, noon: Submission of midterm report
Mar. 10 k-means clustering, Sequential Leader Clustering      
Mar. 12 Sequential Leader Clustering, COBWEB      
Mar. 17 COBWEB      
Mar. 19 Sequences: Introduction, AprioriAll handout, single slide   
Mar. 24 AprioriAll, Reinforcement learning      
Mar. 26 Reinforcement learning      
Mar. 31 Analogy/Case-based reasoning: Introduction, IB3 handout, single slide   
Apr. 2 IB3, Flexible reenactment      
Apr. 7 Flexible reenactment      
Apr. 9 General improvement techniques handout, single slide   
Apr. 14 General improvement techniques handout, single slide Noon: Submission of system and individual report.

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Last Change: 6/4/2020