Tom Dinchak:		Heuristic functions, Alpha-beta pruning, search-strategies and the Prolog Project (how prolog works)
Shreyas Kale		:Learning about the logic behind prolog, greedy algorithms with randomization to improve efficiency, Deep Blue alpha-beta pruning (and how he could figure out what happened in the most recent match between Kasparov and X3D Fritz)
Kalpesh Shah		:Search algorithms, MDPs, Bellman equations. Though AI learning went over my head it was quite interesting.
Shawn Cook:		took the class because of interest in Neural networks (read a book on Synchronous events in nature). Neural networks, perceptrons, multilayer networks was interesting. (Also liked the monte-carlo methods – including computing Pi by dropping a pin on ruled paper)
Robert Wold		Liked the side material and digressions—e.g. the discussion on tabula rasa learning, and how infants learn. Liked the elementary stuff (first part of course) rationally thinking/acting etc.
Steve Commisso:		Progression, A* search, game playing, planning graphs. The fact that the general idea of A* search and heuristics is progressively refined in the first two projects. First time used the real computing power waiting for more than 20 minutes for computer to terminate program execution
Zachery Mortensen:.		being already CSE major in undergrad, liked the algorithm orientation of the class. The discussion of np-hard algorithms in AI. Heuristics and admissibility, applications discussed along with theory. Tried to tell his boss that it is not admissible to pad the estimated effort needed to complete a software project with additional R&R time—but the boss apparently didn’t appreciate. Thought of the connection to Random restart while tuning his violin incrementally, and then when it didn’t quite work, starting over again.
Muhammad Ansari:		Projects helpful. Search alg, min-max, A* heuristics part was interesting. Although didn’t quite like Lisp in the beginning, warmed up to it quite a bit after spending countless hacking hours, and was pleased to have learned a new cool language.
Matt Anderson:		Learnt what intelligence is? More insight on computer’s thinking. Minmax interesting because of chess game relation. Bayesian nets interesting especially the fact that knowing symptoms can help identify causes using these bayes nets. Then hill climbing with random restart. Last project interesting.
Matt hunsaker		: Mapcar with lambda function in Lisp . Simulated annealing, stochastic hill-climbing, Liked thinking of myself as agent and playing to increase utility.
Andrew Call		Was gratified to see that others in the class also thought of the philosophical implications of AI. Project Hw Exams one after another kept busy. Logic. Chess story sent in email was interesting. Tag line: People learned to fly only when they stopped flapping their wings.
Srinivas Vadrevu		:The the flying human example. Ai is more of learning than imitation. Liked NP-completeness discussions, planning graphs, SAT and CSP problems. Particularly liked the part about converting a problem to SAT and let others come up with faster SAT algorithms. Linking theory to practical applications (e.g. Pathfinder)
Thomas Hernandez		Simple memorizing type improvement can help speedup search. Applying the techniques learnt in class such as pattern databases, alpha-beta pruning, weight determining in learning to other scenarios would be interesting
Erin Dow:		Lots of topics covered. Lisp Programming was tough. Alpha-beta pruning interesting—but found to be almost as magical as Red-Black trees.
Saravana kumar:		learning -Bayesian networks, decision trees was interesting. Appreciated the opportunity to re-learn. Thought he knew the learning techniques earlier but taking the class made him realize he didn’t quite know them before. Projects helped learn Lisp and were gradual enough to be interesting
Nils Obermiller:		Solvability using simple algorithms. Hill climbing + restart is interesting. Sherlock holmes/wattson joke and bomb in toilet jokes good
Josh padnick:		Applying AI theory to Real world phenomena. Thought about hill climbing algorithm in traffic (where the guy who cut him off went and got stuck a few blocks down). Surprised by the fully exhaustive search space used by the algorithms CSP, and found applications in his business. Encapsulating domain expertise and Bayes nets are cool
Nicholas Radtke		Found himself appreciating opening slides (the AD-slides) of several of the classes. Prolog project was fascinating. Applications discussions were appreciated. Incredible amount of work. Raspberry bars were good.
Neal Meyer:	First part of course, search strategies were interesting. Using simple DFS to solve problems. Didn’t quite like lisp. Bayesian networks useful and relevant to problems at work. Rao: With understanding comes a sense of loss
Tom Mako	: Autonomous control and neural nets were his interests. Thinks that neural nets will make a big comeback. Lisp is love and hate. Planning graph stuff is interesting.
Menkes van den Briel	Phase transition in Random 3-SAT problem. Deep idea.
Steve Gurasich: ,	Game tree stuff, min-max, alpha-beta, regression/progression planning graph. DPLL. MDP policy construction.
Ravi Bhimavarpu	MDPS and learning chapter. Thought the material was well presented.
Akinduro Temitope	: Stimulated student thinking, by giving partial project code and asking students to add-on to the given code. Tons of Emails everyday from the instructor--while irritating—seemed to suggest he was there everyday.
Yicheng li	Game playing, The fact that AI draws from a collection of multiple disciplines. Connections to neuroscience. Programming in Lisp was fun
Martin Christian:	SAT—and the idea of a combinatorial substrate to which problems can be compiled--was interesting. Was surprised why others didn’t mention SAT more. Email about biases interesting (apparently jived with a class on history).
Jianchun:	Connection between SAT, CSP search strategies, logic programming, probability and propositional inference etc. Heuristic functions
Gabriel Ashe	Neural networks part. Naïve Byes classifier applications. Getting strage looks from other CS students when working on LISP projects.
Nikita Shukin	Basis about agents/search and search algorithms with different heuristics. Alpha-beta pruning/ chess. CSP phase transition. Felt—after the course—that AI is not just a buzz word.
Magdiel Galan	Got into the idea of Maximal Expected Utility and has been bringing it into day to day conversations with his wife and family (not sure how the family is taking it though). Liked Lisp. Planning. Liked the simple backgrounds of people who made seminal contributions. Heuristics, search algorithms
Adam Barr:	Elegance of A* and the projects
Shiva Shankari Janakiraman	Started tabula rasa as far as lisp was concerned, but grew to like those ugly parantheses. Course involved a lot of work, but was considered good. Has been called AI girl, and tried to explain the notion of non-deterministic environments, and dirt emitting vaccums to room-mates when asked to vaccum the room.
Joshua Ruoff	Game trees. Bayes nets. Liked Agent design part.
Mike Mc Fadden	: High level understanding of algorithms. Realizing that there is method to the madness of Prolog..
Jared Phelps	Enjoyed delving deeper into Lisp, the fact that it is possible to have a generic planning program that can plan things given many different worlds, and the min-max algorithm.
Still to come: Will Cushing Richard Wallace James Kerick Kurt Wilcox Vincent Fidducia