COSC 4368 --- Fundamentals of Artificial Intelligence Spring 2023 ( Dr. Eick )

last updated: May 6, 11a

Purpose of this Website

This website intends to satisfy the information requirements of two independent groups: If you have any comments concerning this website, send e-mail to:

Basic Course Information

2023 COSC 4368 Syllabus (last updated on March 31, 2023)
class meets: MO/WE 2:30-4p
class room: S 105 or online MS Teams, COSC4368
Instructor: Dr. Christoph F. Eick
office hours (online using 4368-Class in MS Team): MO 4-4:45p FR 1-2:15p
TA: Md. Mahin
TA office hour: MO 1-2p and TU 11a-noon
TA office: online
TA Email:
TA: Steve Aigbe
TA office hour: MO noon-1p WE 11a-noon
TA office: online
TA Email:
cancelled classes: none at the moment
makeup classes: none at the moment
lectures taught by others: none yet

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, language models, and deep reinforcement learning relying on other teaching material, unless the new 4th edition of our textbook now includes coverage of those topics.

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 2022. 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 22, 2023. 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 2023

Important 2023 Dates COSC 4368

We., January 18, 2:30p: First Course Lecture
We., March 8, 2:30p: Midterm Exam (Review List; March 6, 2023 Review)
March 13+15: Spring Break: no lecture
Mo., March 20: 20-30 minute Lab taught by Steve in preparation of Task3
We., March 22: Lecture on Neural Networks and Autoencoder and Lab in prepation of Task4 taught by Md. Mahin.
Mo., April 10: There will be 30-45 minute lecture on Language Models/Transformers/GPTx taught by Steve and Mahin
Mo., May 1, 2:30p: Last lecture
Fr., May 5,2p: Final Exam (Final 2023 Review List (updated on May 1), May 1, 2023 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, Support Vector Machines and Neural Networks
7. Deep Learning (will cover autoencoders, transformer&language models&GPT Variants and deep reinforcement learning in 2023)
8. AI Politics, Societal and Ethical Aspects of AI
9. Reasoning in Uncertain Environments
10. Planning (only if enough time; not covered in 2023)

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

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

Group Homework Credit (GHC) presentations should take about 12 minutes and should never be longer than 15 minutes. The presentation will be streamed in MS Team 4368-Class. That is, you will join the lecture's MS Teams meeting with your laptop, share your screen and then make your presentation, switching presentators during your presentation. It is okay, if some of your team members present parts of your presentation remotely. Finally, upload your presentation slides in the file section of the 4368-Class channel, dedicated to Group Homework Credit.

COSC 4368 Lecture Transparencies

2023 Polls

Poll1 (January 30, 2023)
Poll2 (April 26, 2023)

2022 COSC 4368 Poll Results

Poll1 Results (conducted on April 13, 2022)

Old 2023 News Items

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


    COSC 2320 or COSC 2430.

    Other Matial Related to COSC 4368

    Some Summaries of the COSC 4368 Questionnaire Responses from January 23, 2019

    Undergraduate Research in Dr. Eick's Research Group

    2022 UH-DAIS Research Overview

    2023 Lecture Attendance

    Attendance counts 3% towards your overall course grade for the Spring 2023 teaching of the course. You have to attend at least 4 F2F lectures in January+February 2023, at least 3 F2F lectures in March and at least 4 F2F lectures in April/May 2023; otherwise a penalty will be assessed. As MS Teams online attendance records mostly have been lost in the first 2 months of the semester and for other reasons, the semester's attendance score will be computed based on F2F attendance as follows: If you attended the minimum number of F2F lectures in the 3 observation periods your attendance number grade will be 95; if you satisified the minimum requirement in 2 periods, your attendance number grade will be 91, if you satisfied the attendance requirement only in one period you number grade will be 79; if you satisfied the minimum attendance requirement not in a single period your attendance number grade will be 67. Basically, the attendance score were computed assuming that you attended all lectures online that you missed F2F! This explains why the attendance number grades are very high this semester.

    2023 Policies Concerning Late Submissions

    Submissions up to 24 hours late receive a 10% penalty; submissions 24 hours and 1 minute to 48 hours late receive a 25% penalty, and submissions received more than 48 hours late will not be graded.

    2002 Exam Solution Sketches

    March 9, 2022 Midterm Exam A Solution Sketches
    March 9, 2022 Midterm Exam B Solution Sketches


    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)

    2022 Problem Sets and Group Project

    Problem Set1 (contains two individual tasks centering on search)

    Learning Paths in a 2-Agent Transportation World using Reinforcement Learning (group project centering on reinforcement learning; duration: February 28-April 26, 2022; 2022 PD World; 2022 Groups) updated on April 6, 2022)

    Problem Set2 (updated on March 28, 5p; two individual tasks: Using Neural Network and SVM for a Classification Task and using Transformers for a sentiment analysis problem; Steve's Transformer Lecture, Transformer Lab)

    Problem Set3 (preliminary first draft; Task5 Grading Rubric)

    Tentative Weights for the different parts of the course in 2022: Problem Sets: 30% , Group Project: 17%, Midterm Exam:21%, Final Exam: 25%, Attendance: 4%, Group Homework Credit: 3%.

    2021 Problem Sets and Group Project

    Problem Set1 (contains individual tasks centering on search, typo in equation C8 has been correct on Feb. 19, 2021 at 1p)

    Problem Set2 (contains individual tasks centering on neural networks, support vector machines and artificial generative networks)

    Problem Set3 centering on decision making in uncertain enviroments and ethical and societal aspects of AI (containing "individual" tasks: no collabortion with other students allowed; Essay Evaluation Criteria for Task 6; take a look at Khadija's How to create and use BBNs in Netica video)

    Learning Paths in a Transportation World (group project centering on reinforcement learning; duration: February 25-April 11, 2021; 2021 PD World)

    The "tentative" weights of the problem sets tasks are as follows: Task1: 12, Task2: 33, Task3: 35, Task 4: 25, Task 5: 28(??), Task 6: 16 and Task 7: 18.

    Older News Items

  • The 2023 attendance policy has been posted below; you are expected to attend 44% of the lectures F2F and the remaining lectures you can attend online. Attendance will count 4% towards your course grade. Moreover, if you miss a small number of lectures that will not have much impact on your attendance grade.


    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.