For those of you who didn't get a chance to do the interactive review in class due to time running out, my apologies.
Please feel free to add your tuppence as comments to this blog thread (and I promise to respond to them as needed--just as I did in the class).
To kick it off, here is the summary of the part of the review that did get said in class--according to Kartik:
CSE471 Interactive Review
- Bayes Nets
- The tool
- Connections between search and theorem proving interesting
- MDPs should be covered more
- They are practical
- WHY DO WE HAVE TO USE LISP?
- Rao: People who are bad at programming are bad at every *kind* of programming
- Lisp does not push users into one paradigm; logic, functional etc. everything are used
- Too much focus on optimality
- Factored representation vs. Black box
- Internal state representation makes a difference!
- Rao: we mostly went from black boxes to factored representations; except for when we considered MDPs, because MDPs consider states as black boxes.
- Discussion was very limited
- Rao: Beyond a certain level, discussion on heuristics is problem-dependent
- Class should be divided into 2 sections
- Requires a lot of learning
- Practical applications;
- for e.g, how do MDPs fit into real-world
- Rao: We fall in the middle of the distribution of courses on AI; we do about 2/3rds of Russell's course at UCB
- Differences in perception between what human mind can do vs. what computers can
- Rao: self-serving descriptions of whatever we do
- Rao: Go is hard for machines vs. chess being easy; but its not clear that Go is somehow more human than chess
- Rao: Drew McDermott's article on Deep Blue; Marvin Minsky's quote "With understanding comes a sense of loss"
- Rao: This entire course was ambivalent about humans, but we need to come back to humans once
- Randomization leading to better results
- Random restart hill climbing search
- Rao: works because there are many solutions
- Bayes' Theorem
- Writing real-life situations is much harder than it seems
- Links between cognitive psychology and AI
- Lot less overlap between what AI seems to be (after this class) and the perception of AI
- Rao: Many of the concepts covered present in mathematics way before AI
- Connections between research techniques and planning
- More discussion on planning would have been nice
- Algorithmic optimality: For search through states etc
- Rao: Problem is most of the algorithms used are NP-Complete
- Rao: AI doesn't pay as much attention to system-building and optimization
- New-found respect for 2^n
- Classifiers, Learning
- Enjoyed homeworks
- Did NOT enjoy projects that required Lisp
- ON LISP