CS 730/730W/830: Introduction to Artificial Intelligence
Professor Wheeler Ruml
TA Scott Kiesel
The goal of this class is to help you learn how to build
intelligent software. We'll cover concepts and algorithms
in areas such as agent architecture, combinatorial search and
decision making, knowledge representation and reasoning, planning,
reasoning under uncertainty, and learning. You should already be a
fluent programmer (as from CS 671), understand common data
structures, and be familiar with basic complexity analysis and big-O
notation.
In spring of 2013, we meet Mondays, Wednesdays, and Fridays
1:10-2:30pm in Kingsbury N204. This class will not be offered in 2014
- stay tuned for spring of 2015.
Handouts
Assignments
All binaries are for 32-bit x86 Linux (like agate.cs.unh.edu).
- Asst 5: handout,
reference solution,
test harness,
harness info,
tiny data,
real data
- Asst 4: handout,
reference solution (only does vi?),
simulators and worlds
- Asst 3: handout,
sample input,
validator,
reference milestone solution,
reference solution.
- Asst 2: handout,
cnf converter,
sample input,
validator,
reference solution
- Asst 1: handout,
milestone validator,
final validator,
reference solution,
world generator,
sample worlds
Generic submission instructions for all assignments are
here.
Bug reports and suggestions regarding assignments are always
appreciated.
Lectures
These lecture videos and slides are from spring of 2012.
- Part 1,
Intro: video, slides: agents
- Part 2, Combinatorial
Search: video,
slides: search, heuristics, CSPs, optimization, games
- Part 3, Certain
Knowledge: video, slides: propositional
logic, reasoning, first-order
logic, resolution, logic
in practice,
- Part 4,
Planning: video, slides: some practical
logics, planning, planning
graphs, regression,
POP, MDPs, solving
MDPs
- Part 5, Learning: video, slides: reinforcement
learning, scaling
RL, supervised
learning, decision
trees, more supervised
learning, unsupervised
learning
- Part 6, Uncertain
knowledge: video, slides: Bayesian
networks, more Bayes
nets, particle
filters, HMMs
- Part 7, Conclusion:
video,
slides: wrap-up
- Bonus: motion planning
Other resources
