CS 730/830: Introduction to Artificial Intelligence

The goal of this class is to help you learn how to build intelligent software. We'll cover concepts and algorithms from perception (learning about the world from data), through planning (deciding what to do), all the way to acting (actually doing something). Along the way, we'll see topics in knowledge representation (how to represent what we know), learning (how to get better), and agent architecture (how to organize all this).

You should already be a fluent programmer in a high-level language (like Java, C++, Scala, or Rust), understand common data structures (like trees, hash tables, and heaps), and be familiar with basic complexity analysis (big-O notation). Contrary to the restriction listed in the course catalog, students outside CEPS who meet these prerequisites are welcome - just talk to me about getting a waiver.

In spring 2025, the class will meet Tuesdays and Thursdays 8:10-9:30am and recitations will meet Fridays 3:10-4pm, all in Parsons N114. The class is expected to be offered every spring for the foreseeable future.

Here are the raw student reviews from 2023.

Handouts

• general information (revised 1/22/24)
• course schedule (revised 2/14/24)
• final project information (revised 3/14/24). Examples of strong final papers from previous years: Bond, Hatem, Leighton, Merrill, Wilt.

Assignments

• Asst 12: handout, test harness, sample problems, reference solution
• Asst 11: handout, test harness, reference solution, image generator, image converter, sample data
• Asst 10: handout, test harness, harness instructions, reference solution, tiny, digits
• Asst 9: handout, test harness, reference solution, sample input
• Asst 8: handout, validator, reference solution, sample input
• Asst 7: handout, validator, reference solution, same cnf converter as asst 6, sample KBs
• Asst 6: handout, validator, reference solution, sample input, cnf converter
• Asst 5: handout, validator, reference solution, 3CNF generator, sample input
• Asst 4: handout, validator, reference solution, graph generator, some graphs, some complete graphs, more benchmarks
• Asst 3: handout, validator, reference solution, sample worlds.
• Asst 2: handout.
• Asst 1: handout, validator, reference solution, world generator, sample worlds.

Bug reports and suggestions regarding assignments are always appreciated.

Lectures

• Part 6, Outro: 4/30, 5/2
• Part 5, Inferring Stuff from Data: 4/9 , 4/11, 4/16 and 4/18, 4/23, 4/25
• Part 4, More Planning: 3/12, 3/14, 3/26 and 3/26, 3/28, 4/2
• Part 3, Knowing Stuff: 2/22, 2/27, 2/29, 3/5, 3/7
• Part 2, Choosing What To Do: 1/25, 1/30, 2/1, 2/6, 2/8, 2/13, 2/15, 2/20
• Part 1, Intro: 1/23

Other resources

• the class Q&A forum
• a simple example of recursive-descent parsing in OCaml and in Python.
• a primer on I/O redirection in Linux
• a unification algorithm, taken from Ed Bender's book Mathematical Methods in Artificial Intelligence (IEEE Computer Society Press, 1996).
• AAAI-24 author kit with properly formatted templates for scientific papers. I also recommend the Overleaf web-based collaborative LaTeX environment.
• links to AI videos/demos
• wondering about research or grad school?
• undergrad research at UNH
• the UNH AI Group
• Robotics at UNH
• Data Science at UNH
• AAAI has a set of overview pages called AI Topics
• Kevin Murphy's nice tutorial on Bayesian networks
• Nick Smith's Guide to Academic Success
• Rao's Intro AI Class at ASU
• Mausam's Intro AI Class at IIT Delhi

Old Assignments from Spring of 2015

• 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

Old Lectures 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