Tentative Syllabus and Grading

CSE 571 Graduate AI at ASU

Tentative Syllabus:

  • Natural language processing using some of the off-the-shelf systems 
    • Link Parser
    • LCC parser 
  • Wordnet
  • Introduction to Weka machine learning tool kit and some of its particular components
  • Knowledge Representation and Reasoning  (Use Book
    • Chapter 1 (Sections 1.1-1.3) .
    • Chapter 2
    • Chapter 3 (Sections 3.1, 3.1.1-3.1.3, 3.1.5, 3.2, 3.2.1, 3.2.4, 3.4, 3.4.1)
    • Chapter 4
    • Chapter 5 (Sections 5.1-5.4, 5.6)
    • Chapter 8 (Sections 8.1-8.3)
  • Other KR & R topics 
    • Reasoning with Bayes nets 
    • Pearl's Probabilistic Causal Models 
    • P-log: Combining logic and probability
    • Semantic Web languages: Combining description logic with rules and non-monotonicity 
  • Learning Causality 

Grading: 

  • Two tests (no finals) - 30%
  • Homework and small warm-up programming assignments - 20%
  • Class participation - 10%
  • Class project - 40%