CS2013-final-report

search, probabilistic reasoning, logical reasoning, and knowledge representation are extremely useful in other
areas even if students don’t continue in the field of AI. Using Lisp exposes them to a functional programming
language and increases their ability to learn a new language and a different way of thinking. Most students
describe the course as one of the most difficult of their undergraduate career, but it also receives very high ratings
in terms of interest and quality.
Body of Knowledge coverage
KA Knowledge Unit Topics Covered Hours
IS Fundamental Issues Intelligence, agents, environments, philosophical issues 4
IS Basic Search Strategies Problem spaces, uninformed/informed/local search, minimax,
constraint satisfaction
4
IS
Basic Knowledge
Representation and
Reasoning
Propositional and first-order logic, resolution theorem proving 5.5
IS Basic Machine Learning Learning tasks, inductive learning, naive Bayes, decision trees 1.5
IS Advanced Search A* search, genetic algorithms, alpha-beta pruning,
expectiminimax
6
IS
Advanced Representation and
Reasoning
Ontologies, nonmonotonic reasoning, situation calculus,
STRIPS and partial-order planning, GraphPlan
3.5
IS Reasoning Under Uncertainty Probability theory, independence, Bayesian networks, exact
inference, decision theory
6
IS Agents Game theory, multi-agent systems 4.5
IS Advanced Machine Learning Nearest-neighbor methods, SVMs, K-means clustering,
learning Bayes nets, reinforcement learning
3
PL Functional Programming Lisp programming 1
Additional topics
N/A
Other comments
Note: Additional electives are offered in Robotics, Machine Learning, Autonomous Agents and Multi-Agent
Systems, and Natural Language Processing.
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