This document is a reference for the pre-reading material,
summarizing the main concepts/ideas that you should understand
before coming to class that day. Generally speaking, you
should have a good grasp of the boldfaced concepts in the
indicated sections. The "Reading Notes" column reminds you
of the reading that you are expected to do for the exam and
provides some additional notes on what is important to know.
Class
Date
Pre-Reading Notes
Reading Notes
1
Tue 1/28
--
Ch. 1; Lisp Ch. 1; McCarthy
paper
2
Th 1/30
Read 2.1, 2.2 intro, and 2.2.1; skim 2.3.1-2.3.2 (task environments)
Ch. 2; Lisp Ch. 2-3; Graham article. You should have a
good understanding of all of the concepts in Ch. 2, which are
foundational. Lisp won't be on the exam.
3
Tue 2/4
Read 3.1 intro, 3.1.1, skim 3.3 intro for main concepts
Ch. 3.1-3.3; Lisp Ch. 4-5, App. A
4
Thu 2/6
Read 3.4 intro, 3.4.1--3.4.3
Ch. 3.4
5
Tue 2/11
Read 3.5 intro, 3.5.1, skim 3.5.2 for main concepts
Ch. 3.5-3.7, Lisp Ch. 7. The exam won't get into the
subtleties of 3.5.3 (IDA*) or the details of the heuristic
generation methods in 3.6.3-3.6.4 but you should understand
why those ideas make sense.
6
Thu 2/13
NO CLASS - SNOW DAY
7
Tue 2/18
Read 4.1 intro, 4.1.1
Ch. 4.1-4.2
8
Thu 2/20
Read Ch. 6 intro, 6.1 intro, and 6.1.1, not worrying about
the mathematical notation but being sure you understand the basic
concepts and the map coloring example.
Ch. 6.1-6.4 (skip 6.3.3); supplementary: Vipin Kumar, "Algorithms
for Constraint Satisfaction Problems: A Survey". Kumar is
useful because it explains things a bit differently than R&N. But
you aren't responsible for any concepts/material in Kumar that aren't
also in R&N.
9
Tue 2/25
Read Ch. 5 intro and 5.1 and make sure you understand
the concept of a game tree and the meaning of "minimax"
Ch. 5.1-5.3, 5.4.1, 5.5.
Section 5.3 (alpha-beta pruning) is especially tricky
and will demand your close attention. Working in a study group to
solve
some examples of alpha-beta game trees will be helpful!!
10
Thu 2/27
Read Ch. 13.2.1-13.2.2 and be sure that you understand
the concepts of random variables, prior probabilities, conditional
probabilities, the product rule, and the joint probability distribution.
Ch. 13. This and Ch. 14 are among the most mathematical
material that we'll cover. It is essential that you
understand all of the math in Ch. 13, or Ch. 14 will be very
hard going!
11
Tue 3/4
Be sure that you really understand Ch. 13!! Glance briefly
at Ch. 14.1 to see where we're headed with representing different
kinds of conditional independence relationships across a set of
random variables.
Ch. 14.1-14.4.2. Chs. 14.1, 14.2, and 14.4.1-2 are very
important
and you should understand everything in those three sections
thoroughly and be able to apply those concepts. You only need to skim 14.3 and understand the
idea of noisy-OR and continuous distributions, not
the math. (You are not responsible for Chs. 14.4.3-4 or
beyond.)
12
Thu 3/6
Read 15.1 to understand the concepts of the Markov assumption
and Markov process. Don't worry too much about the math just yet.
Ch. 15.1-15.2.1, 16.1-16.3. There are a lot of other very
interesting ideas and important AI techniques in the rest of Ch. 15,
but we just don't have time to cover them. Feel free to dabble
if you are interested in reading more (and starting to understand,
say, how a self-driving car might work...). Similarly, multiattribute
utility theory, preferences, and the value of information (rest of
Ch. 16) are basic methods that let us start to think about
decision-making in really complex domains.
13
Tue 3/11
(No pre-reading!)
Ch. 17.5-17.6. The concepts covered in the slides are the most
important to know for the exams. Other material will not be tested
in depth.
14
Thu 3/13
NO CLASS - WATER DAY
15
Tue 3/25
Read Ch. 18.2 to understand the basic idea behind supervised
learning: the problems of classification and regression, search
through a hypothesis space, and the test set / training set distinction.
Ch. 18.1-18.3. You only need to give Ch. 18.3.5 a
cursory read.
16
Thu 3/27
Glance at Ch. 20.1 to see how the basic probability theory that
we've already covered can be used to think of machine learning as a
Bayesian update problem and the discovery of the most likely hypothesis.
Ch. 20.1-20.2. You only need to skim 20.2.6.
17
Tue 4/1
MIDTERM
18
Thu 4/3
--
--
19
Tue 4/8
Review Ch. 7.4.1-7.4.2 (basics of propositional logic),
especially if you had any difficulty with the logic part
of the pretest.
Ch. 7. You need to fully understand 7.1-7.5 and be able
to apply resolution theorem proving to a new problem.
You only need general knowledge of 7.6. Ch. 7.7 is
very important as the foundation for the planning
methods that we will study later
20
Thu 4/10
Review Ch. 8.2, especially if you had difficulty with the
logic part of the pretest. First-order logic is supposed to
be included in CMSC 203, but not every section covers it, so
we will review it carefully. Some of the terminology here may
be new even if the concepts are familiar.
Ch. 8.1-8.3. You should be very familiar with this terminology
and comfortable with applying first-order logic representations to
encode a domain.
21
Tue 4/15
Skim Ch. 9.5 to get a sense of how resolution theorem proving
is done for first-order logic representations.
Ch. 9. You can skip 9.2.2, only skim 9.4.3, and skip
9.4.4-9.4.6. It's important that you understand Ch. 9.5 well
enough to be able to apply resolution methods to a new first-order
logic domain, but you can just skim the proof concepts in
9.5.4 and the discussion of equality in 9.5.5. Ch. 9.5.6 is
important, though, and you should thoroughly understand these
different search strategies for resolution theorem proving!
22
Thu 4/17
-- (no pre-reading for today)
Ch. 12.1-12.2, 12.5-12.6
23
Tue 4/22
PHASE I TOURNAMENT
24
Thu 4/24
-- (no pre-reading for today)
Ch. 10.1-10.2, 10.4.2-10.4.4. All of this material is important!
25
Tue 4/29
-- (no pre-reading for today)
Ch. 17.1-17.3, but you can skip 17.2.3 (convergence of value
iteration) unless you're interested in gaining a deeper understanding.
26
Thu 5/1
-- (no pre-reading for today)
Ch. 21.1-21.3. You can skim 21.2.1, 21.2.2, and 22.3.1. Be sure
you understand temporal difference learning (21.2.3) and Q-learning
(21.3.2). However, you can skip the latter part of 21.3.3, when it
talks about the SARSA variation of Q-learning.
27
Tue 5/6
SEE 4/24 CLASS SLIDES FOR INSTRUCTIONS!!
Turing article
Searle
article
Summary
of Kurzweil's book
Kevin Kelly's critique
28
Thu 5/8
TBA/catchup/review
29
Tue 5/13
PHASE II TOURNAMENT