WQ18 CS 271P LEC A: INTRO ARTIFCL INTEL (34840)

Course Syllabus

WQ17 CS271P: Introduction to Artificial Intelligence

Prof. Richard Lathrop

(Current content and syllabus draw upon previous offerings by

Profs. Lathrop, Ihler, Smyth, Kask, Mjolsness, and Dechter)



 See also the page AI at UC Irvine.

 

Please Note:  This is a Preliminary Syllabus due to Dr. Lathrop's recent illness.  It will be updated and revised as the quarter progresses.



 Place, Time, Instructors:

Place: SSL 140 (building 202 on the UCI campus map)
Time:
Tuesday/Thursday 6:30-7:50pm
Discussion Section:
Friday, 4:00- 4:50pm, in RH 101 (building 200 on the UCI campus map)

 

(If you send email, please put “CS-271P” somewhere in the Subject line.)

 

Instructor: Richard Lathrop, <rickl@uci.edu>

Office hours Tue., 2:00-2:50pm, in DBH-4224, or anytime by appointment

 

Teaching Assistant: Reza Asadi, <rasadi@uci.edu>

(Runs Discussion Section; Supervises Coding Project and Tournament)

Office hours Fri., 3:00-3:50pm, in CS 464A (a.k.a. ICS 464A)

 

Reader: Shraavanth Penugonda, <spenugon@uci.edu>

(Returns Quizzes and Exams)

Office hours Mon., 2:00 to 2:50pm, in CS 464A (a.k.a. ICS 464A)

 

Coding Project/Tournament Assistant Director:

Bradley Sean Morton <bsmorton@uci.edu>

(Assists with Coding Project/Tournament)

Office hours = Project Clinic, below

 

Coding Project Clinic: Mon., 4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)

 

(If you send email, please put “CS-271P” somewhere in the Subject line.)

 


Goal:

The goal of this class is to familiarize you with the basic principles of artificial intelligence. This is a broad introductory survey course. You will learn some basic AI techniques, the problems for which they are applicable, and their limitations.

The course content is organized roughly around what often are considered to be three central pillars of AI: Search, Logic, and Learning. Topics covered include agents, basic search, heuristic search, game search, constraint satisfaction, knowledge representation, logic and inference, probabilistic modeling, and machine learning algorithms.

The Order of Topics will follow roughly your Russell&Norvig textbook (see textbook details below). Lecture and course content sometimes will include other important content not in your text:

Introduction, viewpoints about AI, agents (Ch. 1, 2)

Agents and problem formulations (Ch. 1, 2, 3)

Uninformed search (Ch. 3)

Informed search (Ch. 4)

Game playing (Ch. 5)

Constraint satisfaction (Ch. 6)

Propositional logic (Ch. 7)

Midterm Exam

First Order Logic (Ch. 8)

Inference and Knowledge Representation in First Order Logic (Ch. 9)

Uncertainty, Probability, Bayesian Networks (Ch. 13, 14)

(If time) Machine Learning from observations (Ch. 18)

(If time) Clustering, Regression, Statistical learning (Ch. 20)

Coding Project Due

Final Exam


Class Expected Technical Background:

As a general rule of thumb about prerequisite knowledge, you already should have taken at least one course each in statistics, data structures, single-variable calculus, and boolean algebra.

Also, there will be a required coding project in which you will be supplied a "dumb" coding shell for which you will be required to write the "smarts."  Coding shells are available in C++, Java, and Python.  You should be proficient in coding at least one of those languages.


Class Setup:

The course will be primarily lecture-based. There will be four Quizzes, a Midterm Exam, a Final Exam, and a Coding Project. Dates of the Quizzes and Midterm Exam are given in the detailed class schedule below. The UCI Register sets the Final Exam schedule and the CS-271P time slot currently is listed as TBA. The frequent quizzes are intended to encourage you to stay current with the course material. All exams and quizzes may cover all material presented in class, including lectures and assigned textbook reading. Quizzes will cover mostly material presented since the last quiz. The Final Exam will cover material from the entire course, i.e., the Final Exam will be comprehensive.

Several previous Intro AI quizzes and exams (Files::Past Exams) are made available as study guides to help you learn and master the class material.  Because this class is the first offering of CS-271P it has no history of previous tests, so previous tests from other Intro AI classes were used. Please study them carefully because they are important guides about the performance that will be expected from you now.

An online class discussion forum has been set up on Piazza:

        https://piazza.com/uci/winter2018/cs271p/home

I have never used Piazza before, so I hope those of you with more experience will tell me how to correct any flaws or errors you may find

.

The best way to answer your questions is to ask them in the weekly Discussion Section. If you are confused about something then many other students are also, and so asking questions in Discussion Section will benefit both you and many of your fellows.


Homework:

Homework will be assigned, but is not graded. The reason is that prior student course evaluations alerted me to the existence of student cheating by way of copying the homework answers. I deplore this degree of personal degradation in dishonest students, but I cannot control it, and so I avoid the opportunity. I remain determined to create a fair and honest educational experience for all students, as best I can.


Course Coding Project:

The project coding shells have been posted  (available from page Project_Wumpus_world).  More details soon.
The AI coding project will be a Wumpus World agent (see Ch. 7 in R&N textbook). “Dumb” coding shells are available in C++, Java, and Python. You must write the “smarts.” Project shells and details are here.
This is an individual or pair project, i.e., you must do it entirely by yourself or form a team of two people. Sometimes teams do not work out. Any team member may dissolve their team at any time by so notifying the Instructor, TAs, and Tournament Director. Any code written before the dissolution is “community property” which may be used freely by both former team members. Any code written after the dissolution is the sole property of the person who wrote it.

Please note that you are encouraged to discuss concepts, methods, algorithms, etc.; but you are forbidden to copy: (1) source code from any source, although you are allowed to include standard libraries provided by the environment or compilation environment; or (2) text from any source unless properly cited and set off as a quote. Except for standard libraries and class materials provided from this class website, you or your team must invent and write all of your own code by yourself. Except for properly referenced material, you or your team must write all of your own project report by yourself.

 

Please note that your source code and project report are subject to analysis by automated plagiarism detection programs, and that direct copying will be treated as a violation of academic integrity (please see the section on “Academic Integrity” below).

Please start your AI coding project earlier than you believe necessary, i.e., immediately. It will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

For your project report, please write a short (~2 page) description using the provided template (TBA).


Textbook:

Required: Russell & Norvig : Artificial Intelligence; A Modern Approach, 3rd edition.

The course is based on, and the UCI bookstore has, the 3rd edition. The assigned textbook reading is required, and is fair game for quizzes and exams. You place yourself at a distinct disadvantage if you do not have the textbook. I expect that you have a personal copy of the textbook, and quizzes and exams are written accordingly.

Please purchase or rent your own personal textbook for the quarter (and then resell it back to the UCI Bookstore at the end if you don't want it for reference). Please do not jeopardize your precious educational experience with the false economy of trying to save a few dollars by not having a personal copy of the textbook.

I do deplore the high cost of modern textbooks. You may find the textbook cheaper if you look online at sites such as eBay.com, Amazon.com, etc.; or search the web for other sites related to the textbook.


Grading:

Your grade will be based on a coding project (20%), the four quizzes (20%), a mid-term exam (30%), and a final exam (30%). Homework is assigned but ungraded.  Bonus points are available for filling out a UCI student evaluation of teaching, and for your Coding Project Final AI's performance in the class tournament.

Four Quizzes will be given in the first 20 minutes of class (dates below), and are closed-book, closed-notes. Your lowest quiz score will be discarded in computing your grade. It is not possible to make-up missed quizzes, but one missed quiz may be discarded as your lowest quiz score.

The Midterm Exam will be given in class (date below), and is closed-book, closed-notes. It is not possible to make-up a missed Midterm exam.

The Final Exam will be given in class, (date below), and is closed-book, closed-notes. The Final exam will cover all course material from the entire quarter, i.e., the Final Exam will be comprehensive. It is not possible to make-up a missed Final exam.

Please Note:

I honor all requests made by the UCI Disability Services Center.

Also, I make exceptions for:

  • genuine medical conditions (I require a signed note from your doctor on official letterhead or equivalent documentation, e.g., a scanned copy of official hospital discharge papers),
  • births/deaths in the family (I require a copy of the birth/death certificate or equivalent documentation, e.g., a scanned copy of official funeral papers),
  • jury duty or other court proceedings (I require a copy of your jury service papers or other official court documents), or
  • field maneuvers of the US military or National Guard (I require a copy of your official orders).

Your Coding Project “Final AI” will be entered into a tournament against all of your classmate’s Final AIs. The top 10% will have their Project score increased by 10% (= 2% of total grade), the second 10% by 9% (= 1.8% of total grade), the third 10% by 8% (= 1.6% of total grade), and so on.

Every student who fills out a course evaluation for CS-271P will receive a bonus of 1% added to their final grade, free and clear, off the curve, simply a bonus. EEE will return to me the names of students who fill out evaluations (but not the content, which remains anonymous), provided that enough students fill out evaluations so that anonymity is not compromised. I will add 1% free bonus to the final grade of each such named student.

Student course evaluations are very important to me for monitoring and improving the course content, and for UCI in evaluating our success at our educational mission. *Please* fill out your student course evaluations.


Study Habits:

This course is technical, rigorous, and demanding. You will be expected to learn and master a large body of technical material in a very short period of time. You must demonstrate your mastery by (1) accurate performance on frequent quizzes and exams, and (2) successful implementation of an AI coding project.

I deliberately treat you as adults who are responsible for your own educational decisions, and so Lecture is optional. Discussion Section is required and roll will be taken, because it is part of our educational mission to train our TAs to become future professors. Nevertheless, students who do not attend both Lecture and Discussion Section are at a serious disadvantage and do not succeed as well in this class.

Students who don't do the reading, don't attend Lectures, or spend Lectures and Discussion Section sleeping, on cell phones, surfing the Web, or on social media, are wasting their time and might as well be absent. Such students send me email messages to ask questions that already were covered thoroughly and in detail during Lecture and once again in Discussion Section. On quizzes and exams, they miss points that already have been covered thoroughly.

In stark contrast, students who work hard, over-study, and learn the material, tend to do very well indeed.

Your educational moments are precious, and your education now will be the single most important factor in your future career success or failure. Please, make the most of your precious educational moments now. Please, attend both Lecture and Discussion Section, pay attention, ask questions, and master the material.

The class will move very quickly. Please do not ever fall behind in the class material. Instead, study frequently and diligently. Please begin your AI coding project earlier than you believe necessary; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

Please work harder and study longer. Please understand thoroughly all class material, and ask questions when you do not understand. Please attend all lectures and discussion sections. Please come to lectures and discussion sections prepared with questions about any material that is not clear. Please do all assigned reading, both before and again after lecture. Please review the lecture notes, several times over, both before and again after lecture, until you understand every detail. Please regularly attend office hours with me and the TA. Please ask questions about any class material that is not absolutely crystal clear.

Please work and understand all past quizzes and exams; they are important guides about the performance that will be expected from you now. Please work and understand all the optional homework.

Please OVERSTUDY!!

 


Academic Integrity:

"Learning, research, and scholarship depend upon an environment of academic integrity and honesty. This environment can be maintained only when all participants recognize the importance of upholding the highest ethical standards. All student work, including quizzes, exams, reports, and papers must be the work of the individual receiving credit. Academic dishonesty includes, for example, cheating on examinations or any assignment, plagiarism of any kind (including improper citation of sources), having someone else take an examination or complete an assignment for you (or doing this for someone else), or any activity in which you represent someone else’s work as your own. Violations of academic integrity will be referred to the Office of Academic Integrity and Student Conduct. The impact on your grade will be determined by the individual instructor’s policies. Please familiarize yourself with UCI’s Academic Integrity Policy (https://aisc.uci.edu/policies/academic-integrity/index.php) and speak to your instructor if you have any questions about what is and is not allowed in this course."   --- UCI Academic Senate recommended Syllabus text

Any student who violates the UCI Academic Integrity policies, will receive an academic evaluation of "F" for the entire course, with a letter of explanation to the UCI Academic Integrity Administrative Office.  We seek to create a level playing field for all students.


 

Week Date Event Comments, Slides Readings, Notes
1

Tue 9 Jan

Lecture cancelled due to illness Class setup, Intro Agents (Slides)

R&N Ch 1-2

Cultural Interest

Thu 11 Jan Lecture

Intro State Space Search (Slides)

Uninformed Search (Slides)

R&N 3.1-3.4
Fri 12 Jan Discussion cancelled
2 Tue 16 Jan Lecture Heuristic Search (Slides) R&N 3.5-3.7
Thu 18 Jan Lecture Local Search (Slides)

R&N 4.1-4.2

Cultural Interest

Fri 19 Jan Discussion Review, questions

 

3 Tue 23 Jan

Lecture

Quiz #1

(quiz; key)

Game (Adversarial) Search A (Slides) R&N 5.1, 5.2, 5.4
Thu 25 Jan Lecture Game (Adversarial) Search B (for slides see Game Search A) R&N 5.3 (optional: 5.5+)
Fri 26 Jan Discussion Review, questions, optional HW #2
4 Tue 30 Jan Schedule Adjustment Schedule Adjustment
Thu 1 Feb Schedule Adjustment Schedule Adjustment
Fri 2 Feb Discussion Review, questions, optional HW #3
5 Mon 5 Feb

Coding Project Clinic

4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)
Tue 6 Feb

Lecture

Quiz #2 (quiz; key)

Probability, Uncertainty (Slides) R&N Ch 13
Thu 8 Feb Lecture Bayesian Networks (Slides) R&N Ch. 14.1-14.5
Fri 9 Feb Discussion Review, questions
6 Mon 12 Feb Coding Project Clinic 4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)

 

Tue 13 Feb Lecture Mid-term Exam Review (Slides)

All of above

Cultural Interest

Thu 15 Feb Mid-term Exam (exam; key)
Mid-term Exam
All of above
Fri 16 Feb Discussion Review, questions, optional HW #5
7 Mon 19 Feb Coding Project Clinic 4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)

 

Tue 20 Feb Lecture Propositional Logic A (Slides)

R&N 7.1-7.4

Cultural Interest

Thu 22 Feb Lecture Propositional Logic B (Slides)
R&N 7.5 (optional: 7.6-7.8)
Fri 23 Feb Discussion Review, questions (Slides)
8 Mon 26 Feb

Coding Project Clinic

4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)

 

Tue 27 Feb

Lecture

Quiz #3 (quiz; key)

Predicate Logic Syntax (Slides)

R&N 8.1-8.5

Cultural Interest

Thu 1 Mar Lecture

Predicate Logic Semantics/Inference (Slides)

Predicate Logic Knowledge Engineering (Slides)

Review R&N 8.3-8.5, Read 9.1-9.2, 9.5.1-9.5.5
Fri 2 Mar Discussion Review, questions (Slides)
9 Mon 5 Mar Coding Project Clinic 4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)
Tue 6 Mar Lecture Constraint Satisfaction A (Slides) R&N 6.1-6.4, except 6.3.3
Thu 8 Mar Lecture Constraint Satisfaction B (Slides)

same as CSP A

Cultural Interest

Fri 9 Mar Discussion Review, questions (Slides)
10 Mon 12 Mar

Coding Project Clinic

4:00-4:50pm, in CS 464A (a.k.a. ICS 464A)

 

Tue 13 Mar

Lecture

Quiz #4 (quiz; key)

Intro Machine Learning (Slides)

Optional: Machine Learning Classifiers (Slides)

R&N Ch 18.1-18.4

Cultural Interest

(optional: R&N Ch. 18.5-18.12)

Wed 14 Mar

Happy Pi Day, 3/14

 

 

Thu 15 Mar Lecture

AI Club at UCI (Slides)

Final Exam Review (Slides)

Fri 16 Mar Discussion

Review, questions (Slides)

Instruction ends

11 Tue 20 Mar 6:30-8:30pm Final Exam (exam; key)

Final Exam

All of the above

 

Course Summary:

Date Details Due