Course Syllabus

Syllabus ID: syllabus-b1c3e867d2ce

Course Syllabus
Structured Public Version

ITAI 1370-71

Artificial Intelligence History, Theory, & Platforms
Instructor
Mires, Nicholas
Term
Fall 2026
Department
BAIT
Workflow Status
Published

Instructor Information

Instructor: Blake Mires

Email: miresnb@lamarpa.edu

Phone: none

Office Hours: none

Additional Contact Information: none

Course Description

This course introduces the history, theory, and major platforms of artificial intelligence. Students explore machine learning, neural networks, generative AI, computer vision, ethics, robotics, and real-world AI applications through readings, writing assignments, quizzes, and hands-on projects.

Required Textbook & Materials

Wrench, Jason S., and Sanae Elmoudden. The Future Is Now: Empowering Society Through AI Literacy. Milne Open Textbooks, 2025. ISBN 978-1-956862-20-1.

Textbook Purchasing Statement

Textbook Purchasing Statement: A student attending Lamar State College Port Arthur is not under any obligation to purchase a textbook from the college-affiliated bookstore. The same textbook may also be available from an independent retailer, including an online retailer.

Additional Materials/Resources

none

Pre-requisites/Co-requisites

none

Learning Outcomes

Demonstrate the difference between machine learning and artificial intelligence by providing examples of image segmentation and neural-network training and documenting these examples in a Jupyter Notebook.

Describe the process, meaning, and structure of neural networks, deep learning, and machine learning in a Jupyter Notebook.

Describe an ethical position from which the responsible development of artificial intelligence can proceed by writing an essay about an important issue challenging society.

Describe the role artificial intelligence can play across various computing platforms, including IoT devices, robots, traditional corporate networks, swarms of simple robots or drones, and the World Wide Web.

Configure GitHub and a wiki to document team projects involving the development of an AI or machine-learning platform.

Construct a financial-feasibility analysis for a typical business AI project involving market research.

Program Student Learning Outcomes

Demonstrate the difference between machine learning and artificial intelligence by providing examples of image segmentation and neural-network training and documenting these examples in a Jupyter Notebook.

Describe the process, meaning, and structure of neural networks, deep learning, and machine learning in a Jupyter Notebook.

Describe an ethical position from which the responsible development of artificial intelligence can proceed by writing an essay about an important issue challenging society.

Describe the role artificial intelligence can play across various computing platforms, including IoT devices, robots, traditional corporate networks, swarms of simple robots or drones, and the World Wide Web.

Configure GitHub and a wiki to document team projects involving the development of an AI or machine-learning platform.

Construct a financial-feasibility analysis for a typical business AI project involving market research.

Lecture Topics Outline

WEEKTOPICASSIGNMENTS/QUIZZES (Due on this Date)
Week 1What Is Artificial Intelligence?Due 08/30/2026
Writing Assignment: AI Is Everywhere... Or Is It?
Module 1 Quiz
Week 2The History and Development of Artificial IntelligenceDue 09/06/2026
Writing Assignment: Is AI Actually Intelligent?
Module 2 Quiz
Week 3Jupyter, Google Colab, GitHub, and Basic PythonDue 09/13/2026
Jupyter & GitHub Project
Week 4Algorithms, Data, and Machine LearningDue 09/20/2026
Writing Assignment: Is Every Algorithm Artificial Intelligence?
Module 4 Quiz
Week 5How Machine Learning LearnsDue 09/27/2026
Project: Exploring Supervised Learning in Google Colab
Module 5 Quiz
Week 6Neural NetworksDue 10/04/2026
Writing Assignment: How Does a Neural Network Make a Decision?
Module 6 Quiz
Week 7Training Neural NetworksDue 10/11/2026
Neural Network Training Project
Module 7 Quiz
Midterm Exam
Week 8Computer Vision and Image SegmentationDue 10/18/2026
Image Segmentation Project
Module 8 Quiz
Week 9Natural Language ProcessingDue 10/25/2026
Writing Assignment: How Does AI Understand Human Language?
Module 9 Quiz
Week 10Generative AI and Large Language ModelsDue 11/01/2026
Generative AI and LLM Project
Module 10 Quiz
Week 11AI Ethics and Responsible DevelopmentDue 11/08/2026
Writing Assignment: Responsible AI: What Ethical Principles Should Guide Development?
Module 11 Quiz
Week 12AI Across Computing PlatformsDue 11/15/2026
Writing Assignment: AI Across Computing Platforms
Module 12 Quiz
Week 13Robotics, Drones, and Swarm IntelligenceDue 11/22/2026
Writing Assignment: One Robot or a Swarm? The Promise and Limits of Swarm Robotics
Module 13 Quiz
Thanksgiving WeekThanksgiving Holiday11/29/2026
No assignments due
Week 14Business AI and Financial FeasibilityDue 12/06/2026
RouteWise AI Financial Feasibility Project
Week 15The Future of Artificial IntelligenceDue 12/09/2026
GitHub & Wiki Team Documentation Project
Final Exam

Major Assignments Schedule

WEEKTOPICASSIGNMENTS/QUIZZES (Due on this Date)
Week 1What Is Artificial Intelligence?Due 08/30/2026
Writing Assignment: AI Is Everywhere... Or Is It?
Module 1 Quiz
Week 2The History and Development of Artificial IntelligenceDue 09/06/2026
Writing Assignment: Is AI Actually Intelligent?
Module 2 Quiz
Week 3Jupyter, Google Colab, GitHub, and Basic PythonDue 09/13/2026
Jupyter & GitHub Project
Week 4Algorithms, Data, and Machine LearningDue 09/20/2026
Writing Assignment: Is Every Algorithm Artificial Intelligence?
Module 4 Quiz
Week 5How Machine Learning LearnsDue 09/27/2026
Project: Exploring Supervised Learning in Google Colab
Module 5 Quiz
Week 6Neural NetworksDue 10/04/2026
Writing Assignment: How Does a Neural Network Make a Decision?
Module 6 Quiz
Week 7Training Neural NetworksDue 10/11/2026
Neural Network Training Project
Module 7 Quiz
Midterm Exam
Week 8Computer Vision and Image SegmentationDue 10/18/2026
Image Segmentation Project
Module 8 Quiz
Week 9Natural Language ProcessingDue 10/25/2026
Writing Assignment: How Does AI Understand Human Language?
Module 9 Quiz
Week 10Generative AI and Large Language ModelsDue 11/01/2026
Generative AI and LLM Project
Module 10 Quiz
Week 11AI Ethics and Responsible DevelopmentDue 11/08/2026
Writing Assignment: Responsible AI: What Ethical Principles Should Guide Development?
Module 11 Quiz
Week 12AI Across Computing PlatformsDue 11/15/2026
Writing Assignment: AI Across Computing Platforms
Module 12 Quiz
Week 13Robotics, Drones, and Swarm IntelligenceDue 11/22/2026
Writing Assignment: One Robot or a Swarm? The Promise and Limits of Swarm Robotics
Module 13 Quiz
Thanksgiving WeekThanksgiving Holiday11/29/2026
No assignments due
Week 14Business AI and Financial FeasibilityDue 12/06/2026
RouteWise AI Financial Feasibility Project
Week 15The Future of Artificial IntelligenceDue 12/09/2026
GitHub & Wiki Team Documentation Project
Final Exam

Final Exam Date

Due 12/09/26

Grading Scale

GRADE SCALE

·      90-100    A

·      80-89      B

·      70-79      C

·      60-69      D

·      0-59        F

Determination of Final Grade

·      Writing Assignments  25%

·      Projects                        30%

·      Quizzes                         20%

·      Midterm                      10%

·      Finals                           15%

Instructor Policies

1.      All assignments must be submitted through Blackboard.

2.      All homework, papers, and tests must be completed solely by the student. A first instance of plagiarism will result, at minimum, in a zero on the assignment. A second offense will result in dismissal from the course. (Student not dropped from the course.)

3.      Mature behavior is expected at all times.

4.      Access to Blackboard and your email is required. Frequently check for any new announcements.

5.      Late work will not be accepted except in the case of a documentable emergency beyond the student’s control. Complete assignments and submit them by the due date. You may turn them in early, but not after the due date. Technical difficulties and any other issues must be resolved before the due date, and they will not result in a time extension. If you have issues with Blackboard, use the Technical Support link. Any emails requesting help with an assignment must be sent on a weekday at least 48 hours before the due date. For assignments due over the weekend, emails must be submitted no later than 11:59 p.m. on Wednesday. Emails sent during the weekend will be answered the following work week. Each message must include your name, course, and class section (e.g., PHIL 1301 3A1).

6.   Due dates may need to be altered. I will update the Blackboard calendar if a due date is changed.

 

Attendance Policy

none

Additional Information

none

MyLSCPA

Be sure to check your campus email and Course Homepage using MyLSCPA campus web portal. You can also access your grades, transcripts, academic advisors, degree progress, and other services through MyLSCPA.

Academic Honesty

Academic honesty is expected from all students, and dishonesty in any form will not be tolerated. Please consult the LSCPA policies (Academic Dishonesty section in the Student Handbook) for consequences of academic dishonesty.

ADA Considerations

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Accessibility Services Coordinator, Room 119, in the Ruby Fuller Building. The phone number is (409) 984-6241.

Facility Policies

No food or tobacco products are allowed in the classroom. Only students enrolled in the course are allowed in the classroom, except by special instructor permission. Use of electronic devices is prohibited.

HB 2504

This syllabus is part of LSCPA's efforts to comply with Texas House Bill 2504.

Mandatory Reporting of Child Abuse and Neglect

As per Texas law and LSCPA policy, all LSCPA employees, including faculty, are required to report allegations or disclosures of child abuse or neglect to the designated authorities, which may include a local or state law enforcement agency or the Texas Department of Family Protective Services. For more information about mandatory reporting requirements, see LSCPA's Policy and Procedure Manual.

Title IX and Sexual Misconduct

LSCPA is committed to establishing and maintaining an environment that is free from all forms of sex discrimination, including sexual harassment, sexual violence, and other forms of sexual misconduct. All LSCPA employees, including faculty, have the responsibility to report disclosures of sexual misconduct, including sexual harassment, sexual assault (including rape and acquaintance rape), domestic violence, dating violence, relationship violence, or stalking, to LSCPA's Title IX Coordinator, whose role is to coordinate the college's response to sexual misconduct. For more information about Title IX protections, faculty reporting responsibilities, options for confidential reporting, and the resources available for support visit LSCPA's Title IX website.

Clery Act Crime Reporting

For more information about the Clery Act and crime reporting, see the Annual Security & Fire Safety Report and the Campus Security website.

Grievance / Complaint / Concern

If you have a grievance, complaint, or concern about this course that has not been resolved through discussion with the Instructor, please consult the Department Chair.