zasady: nie modyfikujemy gotowych slajdow tylko wstawiamy, dodajemy do plikow nowe, uzupelniajace

http://aima.cs.berkeley.edu/

niepewnosc

      13 Quantifying Uncertainty
      14 Probabilistic Reasoning 

uczenie:

      18 Learning from Examples
      19 Knowledge in Learning 

computational intelligence:

      sieci neuro        
      alg. genetyczne
      
      automaty komorkowe
      
potem:

   1 Introduction
        2 Intelligent Agents 
        
        7 Logical Agents 
        
           12 Knowledge Representation 
           
     
    
      http://www.cs.cmu.edu/~tom/mlbook.html
      
      #  Ch 1. Introduction. ( postscript  3.8Meg), ( gzipped postscript  317k) (pdf ) ( latex source )

# Ch 2. Concept Learning. ( postscript 347k), ( gzipped postscript 100k) (pdf ) ( latex source ) # Ch 3. Decision Tree Learning. ( postscript 530k), ( gzipped postscript 143k) (pdf ) ( latex source ) # Ch 4. Artificial Neural Networks. ( postscript 1.83Meg), ( gzipped postscript 329k) (pdf ) ( latex source ) # Ch 5. Evaluating Hypotheses. ( postscript 212k), ( gzipped postscript 67k) (pdf ) ( latex source ) # Ch 6. Bayesian Learning. ( postscript 261k), ( gzipped postscript 81k) (pdf ) ( latex source ) see also slides on learning Bayesian networks by Friedman and Goldszmidt.

semantic_web

pl/dydaktyka/miw/2011/slidesai1.txt · ostatnio zmienione: 2017/07/17 08:08 (edycja zewnętrzna)
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