What belongs in a low-math undergraduate AI elective besides ML?
I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell &amp; Norvig's textbook is basically the bible for these sorts of courses.

From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving. 

From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:


Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
Adversarial Game-playing AI (minimax and alpha-beta pruning)
Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT). 


These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")

Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability. 

Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks. 

EDIT: A follow-up; my course has now changed its third unit topic from CSPs/SAT to Markov Decision Processes (Value Iteration and Policy Iteration) and Reinforcement Learning. Its more exciting, leads into ML, and flows out the Adversarial Games nicely because of the shared concept of finding a "policy", especially if Expectimax is covered.