It's time for a computing exam wooo! View this not as an intimidating obstacle to be overcome, but a chance at reflection to see how much you've learned and where your gaps of knowledge may be.

Here's how this is going to go:

• The exam will be entirely conceptual and mathematical to test your understanding of the material.

• The exam will take the place and time of the usual lecture (see the course syllabus) but will last only 90 minutes.

The exam is CLOSED note and CLOSED computer. You may NOT collaborate with peers or any other person during the duration of the exam.

You are allowed to bring ONE 8.5" x 11" (double sided) cheat sheet with any information you'd like with you for consultation into the exam.

The exam may feel long, but that's OK! Take your time, a deep breath or two, and don't worry if you don't finish everything -- it will be likely that your classmates do not either, which will likely be by design.

The exam does not have a forced curve (i.e., that only some set number of people can receive A's, B's, etc.), but it WILL have a difficulty adjustment (upward bonus) if it was too hard.

Exam I will cover the conceptual topics in the first half of the class. These include:

• Propositional Logic: syntax and semantics, definition of models, modus ponens, resolution, theorem proving (both theoretical underpinnings, and proof by contradiction, similar to Classwork 2).

• Probabilistic Logic: syntax, semantics, probability distributions, axioms of probability theory, law of total probability, joint distributions, conditional / posterior distributions, conditioning, independence, conditional independence, Bayes' Theorem (Parts of Classwork 3).

• Bayesian Networks: motivation, structure, semantics, conditional probability tables, Markovian Factorization of the joint distribution, independence claims made by structure, d-separation algorithm, query-difficulty and the techniques used to find different queries, enumeration inference algorithm (and being able to show the steps by hand), as well as simplifications that save on computational efficiency (Parts of Classwork 3 / 4).

• Utility and Decision Theory: utility functions, expected utility, maximium expected utility (MEU), decision networks (structure and semantics), value of perfect information (VPI + computing by hand + properties of VPI) (Classwork 4).

What will NOT be on the exam:

• Python-specific coding problems

• BlindBot-assignment optimization problems like finding best route or having an exploration policy.

The examination format may include:

• Definitions and short answer questions

• Multiple choice

• Problems similar to the past classworks

• Analyzing Bayesian / Decision Networks and computing some probabilities

Be prepared to answer some questions similar to those on the assignments and in-class exercises.

Furthermore, although I won't ask you anything about mechanics we haven't covered in class, you might be expected to apply the mechanics we've learned about in a way that we didn't see in class. If you thoroughly understand the material, there should be no surprises, but still challenges.

Here is my general suggestion for preparation order:

1. Re-read my course notes, re-doing the exercises if you aren't clear on any of them. Importantly: try to answer each "question" box yourself before revealing its answer.

2. Study any available classwork and homework solutions.

3. Form a study group! Together, create variations of problems to answer questions about. E.g., create a Bayesian Network and ask questions of d-sep or enumeration inference.