CS 3600 Introduction to Artificial Intelligence Class Syllabus PDF

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This is the CS 3600 Introduction to Artificial Intelligence Class Syllabus for the Fall 2021 semester with Mark Reidl as the professor....

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8/23/2021

Syllabus for Intro-Artificial Intell - CS-3600-A

Course Syllabus

COVID-19 Information CS 3600 will be offered in-person. If you feel ill, please do not come to class, even if you are uncertain about the cause of the illness. If you are unable to come to class, we will be streaming and recording lectures. We will not check attendance. Exams will be take-home. TA Office Hours will be a mix of in-person and online. Instructor office hours will be held both in-person and on MS Teams. I strongly recommended that you get vaccinated and wear a mask when indoors on campus. Why both? Vaccinations are highly effective but they are not shields; they teach one's body how to fight COVID faster. They are effective in preventing severe hospitalization and death. However, there is still a small possibility that even vaccinated individuals can be infected and transmit COVID even if they don't feel symptoms. According to this tool (https://covid19risk.biosci.gatech.edu/) developed by Georgia Tech researchers, there is > 80% that one will be exposed to COVID in a classroom of 350 students. I am vaccinated, and I will be wearing a mask. I have a 8 year old son who is too young to receive the vaccination and despite vaccination, I do not want to be the vector that introduces the virus to him or through him to his elementary school. In the event that I need to quarantine, class will be held online.

Instructor Mark Riedl Email: [email protected] (mailto:[email protected]) Office Hours: Mondays 1:00pm-2:00pm, and by appointment Office Hours Location: CODA 17th floor Collaborative Core by the spiral stairs and on MS Teams (direct message Mark on MS Teams to request audio or video call). If you are not vaccinated or unwilling to wear a mask during office hours, please opt for a video meeting. (https://teams.microsoft.com/l/team/19%3a1cc971a798434dbca8cd80d0263c39ff%40thread.tacv2/conversations? groupId=7e82ff4f-74cd-471e-9d0d-ad0005b066a7&tenantId=482198bb-ae7b-4b25-8b7a-6d7f32faa083)

Course Time and Location This course will be in-person. There is no attendance requirement. Class will be held Mondays and Wednesdays 9:30-10:45 Eastern Time Zone in Clough 144. Live stream of the class can be accessed here: BlueJeans

(https://primetime.bluejeans.com/a2m/live-

event/yruswsta)

Online slides are available on OneNote here

(https://gtvault-

my.sharepoint.com/:o:/g/personal/mriedl3_gatech_edu/EjCvAhttps://gatech.instructure.com/courses/203678/assignments/syllabus

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Syllabus for Intro-Artificial Intell - CS-3600-A

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I will be using Piazza live (http://piazza.com/gatech/fall2020/cs3600a) functionality during lectures. If you have questions you can post them there during lecture.

Teaching Assistants Teaching assistants will hold office hours in person and online. Check the calendar for the schedule of TA Office Hours: Calendar (Link forthcoming) At other times, you can reach the TAs at Piazza

(http://piazza.com/gatech/fall2021/cs3600a)

(http://piazza.com/gatech/fall2020/cs3600a)

Contact information: Nitya Tarakad ([email protected] (mailto:[email protected]) ) [head TA] Michael Ryan ([email protected] (mailto:[email protected]) ) Manoj Niverthi ([email protected] (mailto:[email protected]) ) Neal Bayya ([email protected] (mailto:[email protected]) ) Jonathan Zheng ([email protected] (mailto:[email protected]) ) Aryan Pariani ([email protected] (mailto:[email protected]) ) Blake Sanie ([email protected] (mailto:[email protected]) ) Sheikh Munim Tazwar Riddhi ([email protected] (mailto:[email protected]) ) Vibhav Bhat ([email protected] (mailto:[email protected]) ) Matthew Yang ([email protected] (mailto:[email protected]) ) Joshua Martin-Jaffe ([email protected] (mailto:[email protected]) ) Eric Gu ([email protected] (mailto:[email protected]) ) Brian Zhu ([email protected] (mailto:[email protected]) ) Louis Castricato ([email protected] (mailto:[email protected]) ) Kaige Xie ([email protected] (mailto:[email protected]) )

General Information Introduction to Artificial Intelligence is a three-credit undergraduate course emphasizing the building of agents, environments, and systems that can be considered as acting intelligently. In particular, you will learn about the methods and tools that will allow you to build complete systems that can interact intelligently with their environment by learning and reasoning about the world.

Objectives There are three primary objectives for the course: To provide a broad survey of AI; To develop a deeper understanding of several major topics in AI; To develop the design and programming skills that will help you to build intelligent artifacts. https://gatech.instructure.com/courses/203678/assignments/syllabus

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Syllabus for Intro-Artificial Intell - CS-3600-A

In practice, you should develop enough basic skills and background that you can pursue any desire you have to learn more about specific areas in IS, whether those areas are planning, knowledge representation, machine learning, vision, robotics or whatever. In particular, this class provides a useful foundation for a number of courses involving intelligence systems, including Machine Learning (CS4641), Knowledge-Based AI (CS4634), Computer Vision (CS4495), Robotics and Perception (CS4632), Natural Language Understanding (CS4650) and Game AI (CS4731).

Prerequisites To succeed at this class, you should know a bit about data structures and algorithms. At the very least, you will have to be able to read pseudocode and understand basic algorithms as they are presented to you. Much of AI concerns itself with finding finding fast algorithms for NP-hard problems. Familiarity with (or at least a lack of abject fear over) some basic computability theory helps to situate many of the algorithms. As the semester continues, a familiarity with basic probability theory will also be very useful; however, we will spend some time on that in class in order to refresh your memory. Finally, you should feel pretty comfortable programming on your own. All projects will be implemented in Python. We will spend no time explaining languages in class; at this point in your career you've been exposed to several programming language and are expected to be able to readily acquire new programming language skills if necessary.

Resources Required Text: Artificial Intelligence: A Modern Approach, Third Edition (the blue book) by Russell & Norvig, 2010. There are significant differences between it and the first two editions (and I have not yet reviewed the newest edition), so be sure to have the right edition. The textbook is a good resource for the things we will cover in class. While I believe I do a good job of introducing the material through lecture, the book provides a secondary description of topics that can reinforce understanding. Online resources: Canvas (here): For syllabus, project code, and critical announcements. GradeScope: For project and exam submission and grading. Piazza (http://piazza.com/gatech/fall2020/cs3600a) : For general questions and discussions. Piazza Live Q&A will be running during lectures so you can ask questions there during lecture. BlueJeans Events (https://primetime.bluejeans.com/a2m/live-event/yruswsta) : For streaming lecture. Please use Piazza for questions during lecture. Microsoft Teams: For online TA and instructor office hours.

Grading Practice Homeworks: Assignments will be made regularly; these will be worth 0% of your final grade. These are for your own good. If you keep up with, and do well on the homework assignments, it is very likely https://gatech.instructure.com/courses/203678/assignments/syllabus

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Syllabus for Intro-Artificial Intell - CS-3600-A

you will do well on the exams. Programming Assignments: (60%) There will be 4 graded projects (15% each) throughout the semester. Many assignments will have extra credit opportunities. Project Wrappers: (10%) Each programming assignment will be followed up by a "project wrapper" (worth 2.5% each) due the week after the project is due. A project wrapper provides an opportunity to reflect on how each specific project connects to other ideas in AI, limitations of the implemented approaches, and broader societal implications. Exams: (30%) There will be a mid-term and a final exam, worth 15% and 15% of your final grade, respectively. They will be take-home exams and you will have at least 48 hours to complete the exams. Late policy: You have four free late days to be used at your discretion throughout the semester. That means you might turn in one assignment two days late or two different assignments one day late, etc. A free late day is "used" one minute after an assignment due date. A second free late day is "used" 24 hours and one minute after the due date. A third free late day is used 48 hours and one minute after the due date. And so on. After the free late days are exhausted, you will receive a 20% penalty per day. Absences due to sickness or GT-approved travel do not count against the late days. Attendance: Attendance will not be recorded and will not be used in assessing grades.

Legaese I reserve the right to modify any of these plans as need be during the course of the class; however, I won't do anything too drastic, and you'll be informed as far in advance as possible. You must abide by the academic honor code of Georgia Tech. Collaboration: Programming assignments and exams are to be completed individually (ie they are not team projects). However, I encourage collaboration. If you collaborate with others, you should acknowledge the collaboration and the nature of the collaboration in source code comments. For example: # George Burdell helped me figure out how to terminate my loop # in main_loop_function() correctly. # I based my tree traversal on stack overflow http://stackoverflow/xyz

You can only have up to three collaborators. We will use automated code plagiarism detection software to compare code to other students as well as commonly used online sources. If we find substantial code copying that is unacknowledged in source code comments, we will refer cases to the Office of Student Integrity. If collaboration is acknowledge but that there is too much similarity, we will provide a warning but not otherwise pursue unless it happens more than once. At the end of the day, you get out of a course what you put into a course. It is your personal judgement as to whether to treat the course as a hoop to jump through or an opportunity to acquire and practice new skills. https://gatech.instructure.com/courses/203678/assignments/syllabus

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Syllabus for Intro-Artificial Intell - CS-3600-A

Code repositories: we strongly encourage the use of using code repositories such as GitHub. Georgia Tech provides it's own secure github repository (https://github.gatech.edu (https://github.gatech.edu/) ) that makes repos private by default. If you use a public repository such as github.com, please make the repo private and password protected. This will protect you from other students who find your code and copy it without your knowledge or permission. This might cause your submission to be flagged for automatic referral to the Office of Student Integrity because you wouldn't have a collaboration acknowledgement.

Calendar of Topics Recordings will be linked on the day it occurs. Week Date 1

Topic

(https://primetime.bluejeans.com/a2m/events/playback/ab5b73bb- Syllabus + 0d23-422d-a304-84e622338049) 8/23

8/25

Intro to AI Intro to AI + Agents

Reading

Due Dates (ET)

ch 1

ch 1, 2 Project 0

2

3

4

Uniformed Search

ch 3.13.4

9/1

Informed Search

ch 3.53.6

9/6

Labor Day

9/8

Informed Search

ch 3.53.6

Informed

ch 3.5-

Search

3.6

Markov decision

ch 17.1-

8/30

9/13

9/15

processes 5

9/20

9/22

Markov decision processes Markov decision processes

6

9/27

https://gatech.instructure.com/courses/203678/assignments/syllabus

Reasoning

17.4

due (ungraded, no submission)

Project 1 due 9/XYZ at 11:59 pm

ch 17.117.4 Project 1 ch 17.1- Wrapper 17.4 due 9/XYZ at 11:59 pm ch 13.15/7

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Syllabus for Intro-Artificial Intell - CS-3600-A

with 13.5 uncertainty 9/29

Reasoning with uncertainty

7

10/4

Reasoning with uncertainty

8

ch 14.114.4 ch 14.114.4

10/6

Probabilistic reasoning ch 15 over time

10/11

Fall Break

Probabilistic reasoning ch 15 over time

9

10/18

Project 2 due 10/XYZ at 11:59pm

Project 2 Wrapper due 10/XYZ at 11:59 pm

Probabilistic reasoning ch 15 over time

10/20

Probabilistic reasoning ch 15 over time Intro to

10

10/25

10/27

machine learning Intro to machine learning

11

12

11/1

Neural networks

11/3

Neural networks

11/8

https://gatech.instructure.com/courses/203678/assignments/syllabus

Midterm exam (takehome) due 10/XYZ at 11:59 pm

ch 18.118.5 ch 18.118.5

Project 3 due 11/XYZ at 11:59pm

Neural networks 6/7

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Syllabus for Intro-Artificial Intell - CS-3600-A

11/10

Project 3 wrapper due 11/XYZ at 11:59 pm

13

(https://primetime.bluejeans.com/a2m/events/playback/9521352b- Advanced

neural nets

a2e6-40cd-baca-8ea6aa408cff) (https://primetime.bluejeans.com/a2m/events/playback/3d6dc37e13e3-4225-adc3-a068502a2ff6)

14

ML recap Societal

11/22

implications

11/24

Holiday

Project 4 due 11/XYZ at 11:59 pm

15

16

11/29

Societal implications

12/1

Reading day

12/6

Final exam 12/10 period

Final exam 8:00 - 10:50 am

Final exam due on 12/XZY at 11:59 pm

Course Summary: Date

Details

https://gatech.instructure.com/courses/203678/assignments/syllabus

Due

7/7...