COSC 4368 --- Fundamentals of Artificial Intelligence Spring 2024
( Dr. Eick )
last updated: April 30, 2024
Purpose of this Website
This website intends to satisfy the information requirements of
two independent groups:
- Students who take the undergraduate AI course
- People that want to find out what AI is,
what its subfields are,
and how its technologies, techniques, and
methodlogies can be used in industrial, government, and
research-oriented environments.
If you have any comments
concerning this website, send e-mail
to: ceick@aol.com
Basic Course Information
2024 COSC 4368 Syllabus
class meets: MO/WE 2:30-4p
class room: SEC 103
Instructor: Dr.
Christoph F. Eick
office hours (online using 4368 MS Team and F2F): MO 4-5p WE 9-10a
office: 573 PGH
TA: Md. Mahin
TA office hour: MO&WE 12:30-1:30p
TA office: online
TA Email: mdmahin3@gmail.com
TA: Raunak Sarbajna
TA office hour: TU&TH 9-10a
TA office: online
TA Email: rsarbajn@CougarNet.UH.EDU
cancelled classes: none at the moment
makeup classes: none at the moment
lectures (and laps) taught by others: March 18: Raunak, March 25: Mahin, April 8: Raunak and April 22: Mahin.
Topics Covered in COSC 4368
The course will give an introduction to AI and it will cover Problem Solving (covering
chapter 3, 4 in part, 5, and 6 in part,
centering on uninformed and informed search, adversarial search and games, A*, alpha-beta search, and
constraint satisfaction problems), Learning (covering learning from examples (chapter 18 in part),
deep learning (extra material) and a lot reinforcement
learning (chapter 21, chapter17 in part;)), Reasoning and Learning in Uncertain
Environments (covers chapters 13, 14, 15 in part, and 20 in part, centering on basics in probabilistic reasoning,
naive Bayesian approaches, belief networks and maybe Hidden Markov
Models (HMM)).
Moreover, the course will cover Evolutionary Computing, Game Theory,
Ethics for AI, Deep Learning centering on autoencoders, diffusion models and U-Net relying on other teaching material.
Course Materials
Recommended Text:
- S. Russell and P. Norvig, Artificial Intelligence, A
Modern Approach, Fourth Edition,
- Prentice Hall/Allyn&Bacon, December 2020,
-
Link to Textbook Homepage.
Course Elements
There will be a midterm and a final exam in Spring 2024. This semester we will have 3 problem sets which contain tasks which require
programming, and tasks which use AI tools, and an essay writing task. There will be six tasks
in the three problem sets! There will be a 7-week group project which will start approx. February 20, 2024.
Finally, each student will be involved in a single group homework credit (GHC) task (which are also group tasks), whose
solution needs to be presented during the COSC 4368 lecture. Each group will solve a different "kind of homework" problem!
News COSC 4368 Spring 2024
- Make sure that your GHC documents are uploaded in the respective channel of the 4368 Teams page, as Dr. Eick
will be grading the GHC submissions starting May 6.
- The location of the May 6, 2-4p COSC 4368 Final Exam is: F(Fleming) 160! A detailed review list for the final exam
will be posted on this website by Tuesday, April 30, noon the latest!
- Please submit teaching evaluations for this course, and the other courses you take this semester!
- Task7 is due on We., May 1 end of the day!
- The classes on April 24 and 29 will discuss belief networks, hidden Markov models, and AI Arms Races. Moreover,
the will be a 35 minute review for the May 6 final exam on April 29.
- GHC topics for groups L, O and N have been posted below.
- The midterm exam has been finally graded; its
number grade average was 80.11. We apologize for the delay! A link to solution sketches for the midterm exam can be found
in the next section, below!
Important 2024 Dates for COSC 4368
We., January 17, 2:30p: First Course Lecture
We., March 6, 2:30p: Midterm Exam (2024 Review List;
March 4, 2024 Review, Solution Sketches MT2024)
March 11+13: Spring Break: no lecture
Mo., March 18: Lecture and Task3 Lab, centering on Neural Networks, taught by Raunak.
We., March 27: Lecture on Autoencoder and Task4 Lab taught by Md. Mahin.
Sa., April 6, 11:59p: Deadline Task4
Su., April 14, 11:59p: Deadline Group Project
Mo., April 15: Lecture and Task5 Lab, centering on Diffusion Models, taught by Raunak.
Mo., April 22, 11:59p: Deadline Task6
Su., April 28, 11:59p: Deadline Task5
Mo., April 29, 2:30p: Last lecture
We., May 1, 11:59p: Deadline Task 7
Mo., May 6,2-4p: Final Exam in F (Fleming) 160 (Final 2024 Review List (updated on May 1),
April 29 Review for Final Exam)
Tentative Course Organization
1. Introduction to AI
2. Search
3. Evolutionary Computing
4. Game Theory (very short)
5. Reinforcement Learning
6. Supervised Learning, centering on Basics and Neural Networks
7. Deep Learning (will cover autoencoders, diffusion models, and briefly U-Net,
and language models)
8. AI Politics and Societal/Ethical Aspects of AI
9. Reasoning in Uncertain Environments
10. Planning (only if enough time; not covered in 2023 and 2024)
2024 Problem Sets and Group Project
Problem Set1 (two individual tasks
centering on search)
Problem Set2 (individual tasks
centering on neural networks and deep learning)
Group Project: Reinforcement Learning in a 3-Agent Transportation World
(2024 PD-World, 2024 Groups,
February 26-April 12, 2024)
Problem Set3: Ethics and Societal Aspects of
AI and Belief Networks (Task 6
Grading Rubric; Task 7: take a
look at Khadija's How to create and use BBNs in Netica video))
2024 Policies Concerning Late Submissions
Submissions up to 24 hours late receive a 8% penalty; submissions 24 hours and 1 minute to 48 hours late receive a 20% penalty,
and submissions received more than 48 hours late will not be graded.
2024 Group Homework Credit Tasks
2024 Groups
Tasks (will be posted at least 5 days before the presentation date):
Group A will present on We., Feb. 7 (task can be found as a slide in search1.pptx)
Groups B and C Tasks (both groups will present on Mo., Feb. 12)
Group D Task (will present on We., Feb. 14)
Group E Task and Group F
Task (both groups will present on Feb. 28)
Group G Task (will present on March 4)
Group H will make a presentation and lead an in-class discussion centering on
"Using ChatGPT in COSC Courses" and Group I Task (both groups will present on March 20)
Group J "ImageFX: Demo & Brief Look under its Hood" and
Group K Task) will present on April 3
Group M will give a presentation and lead a discussion about AI Arms Races on April 24!
Groups L, O, and N Task (group L will present of April 17, and groups O will
present on April 24, Group N will give a presentation on April 29)
2024 COSC 4368 Polls
January 24 Poll Results
Undergraduate Research in Dr. Eick's Research Group
2024 UH-DAIS Research Overview
Some Summer 2024 Undergraduate Research Topics (only Topic1 is
still available)
COSC 4368 Lecture Transparencies
- 2024 Introduction to AI and Course Information
COSC 4368 (updated on January 16, 2024;
will be used for first two lectures in 2024); see also Dr. Eick's 2019 AI Talk"!
- 2024 Search Transparencies:
- Search1 (Classification of Search Problems, Terminology, and Overview
),
Search2 (Problem Solving Agents),
Search3 (Heuristic Search, Exploration and Local Search),
Search4 (Randomized Hill Climbing and Backtracking; not covered in textbook),
Backtracking Wiki (to be discussed in review for midterm exam),
Search5: Games (credit for
almost all slides goes to ai.berkely.edu, reduced coverage in 2022),
Search5a (Brief Discussion of Bridge and
Man vs. Machine Game Contests; not initally covered in 2024),
Search6: Constraints Satisfaction Problems (credit for
some slides goes to ai.berkeley.edu),
Search6a (Dhar & Quale's paper on Dependency Directed
Backtracking (DDBT); not covered in 2024),
Search7: More on Expansion Search (only material which
centers on greedy search and A* will be covered in 2024),
Search8 (Kamil on Backtracking; not covered),
Suggestions for Solving the Rook+King vs. King Endgame (WRKBK) Problem
(not discussed in 2024).
- 2024 Teaching Material on Evolutionary
Computing (EC): EC1: Introduction
to Evolutionary Computing (by Eiben and Smith covering Chapter 3 of their book)
and EC2:Example: Using EC to Solve Travelling
Salesman Problems, Eiben-Smith Introduction to EA (they
call 'EC': 'EA'!), April 6 EA-paper Walkthrough Notes.
- 2024 Game Theory Slides: G1: Introduction to
Gametheory (USC Economics slide show
by Shivendra Awasthi (???), will be used in the lecture) and G2:
Mo Tanweer Mohammed's Introduction to Game
Theory (not used in the
COSC 4368 lecture).
- 2024 Machine Learning Coverage:
- A Gentle Introduction to
Machine Learning
- Reinforcement Learning: RL1 (Introduction to Reinforcment Learning),
Deep Reinforcement Learning: Neural Networks for Learning Control Laws
(by Steve Brunton; will watch the first six minutes of this video which introduces deep reinforcement learning, and
resume watching the video at 13:49 which discusses Alpha-Go and Other Applications of Reinforcement Learning),
Reinforcement Learning from Human Feedback: From Zero to ChatGPT (by
HugginFace; will just watch 5:50-11:00 of this video which discusses how reinforcement learning is used
to train language models, such as GPT),
2019 Soccer RoboCup, Robo Cup,
RL2 (Using Reinforcement
Learning for Robot Soccer; not covered in 2023), RL3
(Kaelbling's RL Survey Article: particularly, read sections 1, 2, 3, 4.1, 4.2, 8.1 and 9);
Steve Brunton's
Introductory Video to Reinforcement (might be used in 2024).
- Introduction to Supervised Learning (also
called "Learning from Examples")
- Neural Networks: Introduction to Neural Networks (covered on March 18, 2024),
NN1
(3blue1brown: What is a
Neural Network? (suggest you watch this video, if you did not have any exposure to NN before)),
Neural Networks (Dr. Eick's "old" 2023 NN slides),
NN3 (Russel's Introduction to Neural Networks,
not covered in the lecture, but you might take a look at it).
- Support Vector Machines (not covered in 2024,
Review of the SVM lecture).
- 2024 Deep Learning Coverage: Introduction to Generative AI (Google
DeepMind Lecture by Ruiqi Gao); Neural Network Basics and Short Introduction
to Deep Learning; AutoEncoders (Mahin's March 27, 2024 AutoEncoder Lecture,
Mahin's Autoencoder Notebook);
Diffusion Models (Lecture (the first 35 slides were covered
in the lecture), Demo,
ipynb File); Mahin's April 22, 2024 Language Model lecture.
The following Deep Learning slide sets were not covered in 2024: Transformers (Transformer
Basics); Mahin's April 10, 2023 GPT Overview,
Transformer
Lab);
Convolutional Neural Networks(CNN
CNN Article, Second CNN Article,
CNN Video for Beginners).
- 2019 Logical Reasoning Transparencies (not covered anymore!):
- 2024 Reasoning in Uncertain Environments
Transparencies
2024 Societal and Ethical Issues of AI and AI Politics
2020 Planning Slides: Introduction to Planning (based on a lecture by Jim Blythe,
Jose Luis Ambite,Yolanda Gil; likely not covered in 2024)
2017 Last Words
2006/2009 Soft Computing Transparencies
A quick look to Knowledge-based Systems
Foundations of AI (quite short; to
be discussed in the last class of the semester)
2009: Topics covered and
not covered in COSC 6368.
Dec. 7, 2004 Review for the final exam;
2024 Lecture Attendance
Attendance counts 3% towards your overall
course grade for the Spring 2024 teaching of the course. F2F Attendance will be taken Jan. 29-April 29, 2024;
that is twice in January, 8 times in February, 5 times in March, and 9 times in April for a total of 24;
number of lectures you attended will be converted as follows into a number grade:
23-24:94, 22:93, 21:92, 20:89, 19:86, 18:83, 17:80, 16:77, 15:74, 14:71, 13:68, 12:65, 11:62, 10:59, 9:56, 8:53, 7:50,
6:47, 0-5:44.
Old 2024 News Items
- The group project's submission deadline has been extended and finalized to Su., April 14; groups with 3 members (due to
dropping of a group member) get 2 extra days.
- Optional GHC Task: Overview about Techniques, Algorithms, and Frameworks used in the AlphaGo Program (to be presented on April 15;
Silver's AlphaGo Nature Paper;
Video about the AlphaGo Paper.) Groups K, L, M, N and O: If you like to present this topic (as your GHC Task)
send Dr. Eick an e-mail by March 31 the latest!
- Please Dr. Eick or the course TAs via e-mail, rather using MS Teams; especially, if in more important cases.
- Currently, you mostly see the course website of the Spring 2023 teaching of this course; this website will
be updated incrementally as we move along with teaching the course in Spring 2024. The course topics covered in
Spring 2024 will not be significantly different from those covered in Spring 2023; however, we will
be covering two new deep learning topics: diffusion models and convolutional neural networks (CNN),
centering on U-Net. Moreover, we will discuss language models at a non-technical level in 2024,
centering on what they are and what they can be used for.
- The course will use MS Teams called "H_20241_COSC_4368_12510" to managage assignment submission, for
managing and displaying grades and as a chat platform. There seem
to be at the moment some technical difficulties with adding enrolled students to this team. However, if
you are not enrolled in this team by Monday, January 22 end of the day, contact the course TAs or
Dr. Eick to add you manually to this team!
Reinforcement Learning Videos
Please view the following 3 videos:
Siraj Raval: How to use Q Learning in Video Games Easily (7 minutes, will show the first 3:30 on February 20, 2019)
Richard Sutton: Deconstructing Reinforcement Learning (about 50 minutes)
Eric Guimarães:Demo Q-Learning in a GridWorld(2 minutes)
2019 4368 Review Solution Sketches
Solution Sketches April 8, 2019 Review for Midterm2 Exam
Prerequisites
COSC 2320 or COSC 2430.
Other Matial Related to COSC 4368
Some Summaries of
the COSC 4368 Questionnaire Responses from January 23, 2019
2002 Exam Solution Sketches
March 9, 2022 Midterm Exam A Solution Sketches
March 9, 2022 Midterm Exam B Solution Sketches
Grading
Translation number to letter grades:
A:100-92 A-:92-88 B+:88-84 B:84-80 B-:80-76 C+:76-71
C: 71-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0
Only machine written solutions to homeworks and project reports
are accepted. Be aware of the fact that our
only source of information is what you have turned in. If we are not capable to understand your
approach or solution, you will receive a low score.
Moreover, students should not throw away returned assignments or tests.
Students may discuss course material and homeworks, but must take special
care to discern the difference between collaborating in order to increase
understanding of course materials and collaborating on the homework /
course project
itself. We encourage students to help each other understand course
material to clarify the meaning of homework problems or to discuss
problem-solving strategies, but it is not permissible for one
student to help or be helped by another student in working through
homework problems and in the course project. If, in discussing course materials and problems,
students believe that their like-mindedness from such discussions could be
construed as collaboration on their assignments, students must cite each
other, briefly explaining the extent of their collaboration. Any
assistance that is not given proper citation may be considered a violation
of the Honor Code, and might result in grade reduction, obtaining a grade of F
in the course, and in further prosecution.
2020 Reviews and Exams
Midterm1 Exam (Mo., March 2, 2020 Review List,
February
26, 2020 Review for Midterm1 Exam)
Midterm2 Exam (Mo., April 13, 2020 Review List,
April 8, 2020 Review
for Midterm2 Exam (only 40% of the review questions will be discussed on April 8!)).
Final Exam (Mo., May 4, 2p, Review List 2020 Final Exam,
April 27,
2020 Review for Final Exam, Solution Sketches May 6, 2019 Final Exam)
2021 Final Exam and Review for it
Final Exam (We. May 12, 2022 2p, First Draft of Review List 2021 Final Exam (will
be finalized by May 6 the latest,
May 3
2021 Review for Final Exam, Solution Sketches May 6, 2019 Final Exam)
2023 Problem Sets and Group Project
Problem Set1 (two individual tasks
centering on search; updated on Feb. 2)
Problem Set2 (two individual tasks
centering on supervised learning and generators/autoencoders; Steve's 2023 Task3 Lecture,
Task3 Jupyter
Notebook, Task4 Jupyter
Notebook; you find the March 22 Autoencoder lecture in the deep learning slides below)
Problem Set3 (Task5
Grading Rubric; Task6: take a
look at Khadija's How to create and use BBNs in Netica video))
2023 Group Project (February 24-April 23, 2023): Learning Paths in a 2-Agent 3D
Transportation World using Reinforcement Learning
(2023 PD World, 2023 Teams)
Tentative Weights in 2023 (subject to change): Problem Set Tasks: 30%, Group Project:17%, Midterm Exam: 21%,
Final Exam: 26%, GHC: 3%, Attendance: 3%.
2023 Group Homework Credit Tasks
2023 Groups
Tasks
Group A and Group B Tasks (both groups will present on We., Feb. 8;
Group B will present a revision of their solution on We., Feb. 15)
Group C Task (will present on Mo., Feb. 13)
Group D Task (will present on We., Feb. 22)
Group E Task (will present on We., March 1)
Group F Task (will present on Mo., March 6 (and maybe March 20))
Group G and H Task (will present on Mo., March 27)
Group I Task (will present on Mo., April 3)
Group J will make a 10-13 minute presentation "Will China be the Number 1 in AI", followed by a discussion,
on We., April 12!
Group N will discuss ChatGPT Mo., April 17
Group K will give a presentation on Robot Soccer on Mo., April 24
Group O will give a presentation on 'AI and Fake News' on We., April 26
Group L Task (will present on Mo., May 1)
Group M will give a presentation about the European AI Ethics Guidelines on May 1
Miscellaneous
Finally, Congratulations go to all 4368 students who graduated in Spring 2020
semester!!! This slide is part of the "must see"
Spring 2020 NSM Graduation Celebration video.