Classes

This web page contains information about the lectures: the topics covered/to be covered, when it was/will be covered, the readings assigned, homework assigned/due, and slides and handouts as they become available.

In the readings, RN is the textbook, NN is the online book Neural Networks and Deep Learning (Michael A. Nielsen, Determination Press, 2015): Really good textbook for learning neural networks. (Available free online at neuralnetworksanddeeplearning.com.)

Date Lecture topic Reading Assigned Due Lecture materials
1/17 What is AI? RN: Ch1     Slides
1/19 Agents RN: Ch2     Slides
           
1/24 -SNOW DAY-        
1/26 Search RN: Ch3.1–3.4 Project   Slides, Notes
  Lisp introduction       Slides
           
1/31 Uninformed search RN: Ch3.4 Basic agent Lisp Slides
2/2 Heuristic search       Slides
           
2/7 CSP RN: Ch6.1–6.3     Slides
2/9 -SNOW DAY-        
           
2/14 -SNOW DAY-        
2/16 Game-playing RN: Ch5.1-5.3;5.5;5.7 Search agents Basic agent Slides
           
2/21 Genetic algorithms RN: Ch4.1.4     Slides
2/23 Neural networks RN Ch18.7.1-18.7.3; NN Ch1     Slides
           
2/28 Neural networks       Slides (Updated)
3/2 Symbolic reasoning: Knowledge       Slides
           
(3/2-3) PRELIM I on Blackboard        
(3/5) ----     Search agents  
           
3/7 SPRING BREAK        
3/9 SPRING BREAK        
           
3/14 SPRING BREAK        
3/16 SPRING BREAK        
           
3/21 Logic/Theorem proving RN: Chapters 7–9 HW: Neural Nets   Slides
3/23 Theorem proving/RBES        
           
3/28 RBES        
3/30 Knowledge representation RN: Ch12 (skim) HW: RTP Neural Nets Sym. Reas. Slides (updated)
           
4/4 KREP/Planning RN: Ch10, 11      
4/6 Planning        
           
4/11 Planning       Planning slides (new)
4/13 Uncertain reasoning overview RN: Ch13, 14 (skim)   RTP (now: 4/16) Slides
           
4/18 PRELIM II sample prelim      
4/20 Convolutional neural networks Coursera lectures by Geoffrey      
    Hinton: lectures 5.1–5.4      
           
4/25 Perception: Intro to NLP RN: Ch23.1-23.3     Slides
4/27 Perception: CNN, RNN, LSTM nets (Coursera lectures above)     Slides
           
5/2 Intro to non-NN learning RN: Ch18.1-18.5     Slides
5/4 Intro to MAS (none)     Slides

Author: Roy M. Turner

Created: 2017-05-07 Sun 20:12

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