Midterm Examination 2 |
CSE 415: Introduction to Artificial Intelligence The University of Washington, Seattle, Winter 2009 |
Date: Friday, March 6. |
Format: The second exam will be similar in format and length to the midterm exam. The topics covered will be drawn from the following, which includes some topics from the first part of the course and some from the second. |
Topics:
State-space search states, state spaces, operators, preconditions, moves, heuristic evaluation functions, iterative depth-first search, recursive depth-first search, breadth-first search, best-first search, uniform-cost search, iterative deepening, A* search. Minimax search for 2-player, zero-sum games Static evaluation functions Backed up values Alpha-beta pruning Zobrist hashing Predicate logic Interpretations, satisfiability, consistency, models Horn clauses PROLOG syntax Probabilistic reasoning Bayes' rule Odds and conversion between odds and probability Bayes nets Image understanding Human vision: subjective contour illusion, pareidolia Shannon sampling Quantization Histograms Thresholding Valley method for threshold selection Hough transform polar coordinates representation of lines parameter space array voting process peak detection Edge Detection with the Roberts Cross Operator Four-connectedness, eight-connectedness of sets of pixels Formal Segmentation into Regions Morphology transformations erosion dilation opening closing Scene analysis with Guzman's labelling method Perceptrons How to compute AND, OR, and NOT. Simple pattern recognition (e.g., 5 x 5 binary image inputs for optical character recognition) Training sets, training sequences, and the perceptron training algorithm. Linear separability and the perceptron training theorem. |