Tentative Syllabus and Grading

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%