Syllabus

BASIC COURSE INFORMATION

  • Semester: Spring 2024
  • Credits:
    • 3 credits, COMP 841
    • 4 credits, COMP 741
  • Prerequisites:
    • COMP 741: COMP 525 Data Structures Fundamentals or equivalent
  • Department: Applied Engineering and Sciences Department

INSTRUCTOR INFORMATION

  • Mihaela Sabin, Ph.D., Professor of Computer Science

How to get in touch with the instructor

  • Email the instructor using Canvas Inbox tool
  • Schedule an in-person or Zoom meeting
  • Meet after class during the help session.

COURSE DESCRIPTION

Catalog Course Description

Balancing the science of AI with its engineering applications, the course focuses on AI foundations and principles for building intelligent computational systems. Reasoning, planning, learning, explaining, and acting with certainty and uncertainty are AI areas in which students will practice how to build AI systems that solve real-world problems. Particular attention is given to the impact of AI applications on our society and related ethical, privacy, security, and safety implications.

Course Goals

The purpose of the course is for you to learn what AI is and what it means, in a practical and consequential sense and at all levels: personal, institutional, societal, and at all of humanity levels. Through various forms of communication and collaboration, and through development activities we will examine AI’s foundations and principles, areas of research, and how AI applications are built and deployed to solve problems.

Competencies

Learning in the course will help you achieve the following professional competencies:

  • Explore and critique AI applications and their impacts on individuals, communities, society, and humankind.
  • Read and analyze relevant AI literature disseminated through journal articles, conference proceedings papers, and popular media. Present, discuss, and evaluate AI approaches and technologies.
  • Examine, annotate, and evaluate the theoretical basis, design decisions, and implementation of open-source AI applications.
  • Participate in and bring your own contribution to the development of a team project that demonstrates the applicability of neural and symbolic AI approaches.
  • Practice with and develop personal qualities and behavioral patterns that are highly regarded in the workplace, such as being responsible, persistent, adaptable, and self-reflective.

Modality

  • In-person, scheduled weekly class meetings
    • Location: Room 149
    • Day/Time: Tuesday, 1:10 - 3:00 pm
  • In-person and online engaged time and learning activities outside class.

Estimated Student Workload

This syllabus reflects the federal definition of 1 credit hour, that is:

1 credit hour = 3 hours of academic work per week per 1 credit over a 15-week semester.

This means that the student’s weekly workload, including class time, is estimated at:

  • 9 hours per week for graduate students
  • 12 hours per week for the undergraduate students.

In-Class Learning

Weekly class meetings are primarily dedicated to presentations, discussions, live coding, working in pairs or small groups, and reflections. Therefore, teaching will use minimal lecturing. The expectation for productive in-class learning is that you come prepared to class meeting by fully engaging in outside class learning.

Outside Class Learning

Outside class time is dedicated to:

  • Guided, collaborative, and independent study activities
    Work to complete your assignments and reflection on your learning experience
  • Participation in ongoing communication supported by various communication platforms, including Canvas and GitHub.
  • One-on-one check-ins with class instructor to evaluate your progress
  • Study group work, tutoring sessions, and consultation with other supports available on Manchester campus, such as CAE, Library, and classroom assistants.

Simultaneous 741/841 Course Sections

Undergraduate section COMP 741 M1 is cross listed with the graduate section COMP 841 M1. These sections are taught simultaneously to both undergraduate and graduate students.

  • The content of both sections is the same in terms of goals and competencies, course schedule, and types of learning activities, whether in class and outside class.
  • The requirements and expectations of the student work, however, differ substantially with assignments, labs, presentations, and team project. These differences are clearly stated in the assigned work requirements and in the evaluation rubrics that accompany assigned work.

Graduate students are expected to demonstrate a deeper understanding of AI foundations and principles and higher proficiency of computing skills in building intelligent computation systems.

Academic Integrity

Completing your own work is essential to learning. Copying the work of others is not learning. You are expected to do your own work and not submit as yours something done by others.

Individual products of your learning in this class (reading notes, design documents, code, and reflections) or individual contributions to collaborative work (e.g., team project) must be entirely done by you. You cannot submit as yours something done by others, obtained from external sources, or generated by AI genrative pretrained transformes (GPT) tools.

Collaborative work has clear requirements regarding the nature of collaboration. Grading is based on your individual contribution to the collaborative work. If unclear, you must consult with the course instructor on what is allowed. It is your responsibility to get such clarification.

Whether done individually or in collaboration, submitted coursework must ALWAYS give clear attribution to the source(s) of content included or integrated in your work.

The instructor will reduce your grade for work that does not include proper attribution. Giving attribution has many forms, depending on how content that is NOT yours is used in your work. Thus, you may need to:

  • Cite the content that originates from other sources or has been modified and integrated in your work.
  • Reference the source(s) you have used:
    • Articles, forum or blog posts, GitHub repos, tutorials, and other accessible materials, regardless of modality (written, audio or video streaming)
    • Generated content obtained from AI tools, such as Generative Pretrained Transformers (GPT) and the like.
  • Give credit to individuals who have helped you, whether peers,ntutors, lab/tech assistants, course instructor, or any other person (friend, relative, etc.)

Do not work on behalf of someone else and do not provide your work products to others.
If you do, you commit an act of academic integrity misconduct. There is no way
to know whether those who get your work products intend to submit them as theirs. Equally important, this is NOT how you help someone learn.

There are consequences if you deviate from the course and university academic integrity policy. For academic integrity misconduct, you may receive no credit for the assignment in question. Persistent academic integrity misconduct will result in you failing the course.

You will receive notice of the academic misconduct allegation from the course instructor. The course instructor will meet with you and give you the opportunity to respond. If the violation stands, the course instructor will report it to the Office of Community Standards.

Bottom line, do not cheat, plagiarize, or faciliate academic integrity misconduct. It is very important that you review the University’s Academic Integrity policy.

COURSE REQUIREMENTS AND ASSESSMENT OVERVIEW

Student learning is assessed based on evidence produced by the following activities. Percentages show how much each activity contributes to the final grade.

Participation (10% Bonus)

In-class learning encourages and supports everyone’s participation. Outside class learning can rely on participation in study group activities, open lab work, tutoring sessions, and discussions facilitated by the use of online chat/text messaging/forum tools selected by students in consultation with the course instructor. One-on-one check-ins with the instructor during the semester provide additional assistance.

Assigned Reading and Reading Notes (15%)

The first exposure to AI disciplinary content happens through the assigned reading. They are important for all the learning activities in this course: class meeting discussions, individual presentations, lab activities, and the team project. All students are expected to prepare notes that summarize key concepts, principles, models, and techniques, along with relevant questions and implications derived from the reading material. Reading notes are entirely your own individual work.

The reading notes:

  • Are developed in two steps over two weeks and
  • Include a reading comprehension session with experts from the CAE.

The first draft (RN Feedback) is due the following week on Friday, by 5 pm. The final notes are due the week following the draft submission deadline and no later than midnight BEFORE class.

The reading notes are NOT accepted after the deadline.

Lab Projects (25%)

To consolidate learning of AI concepts, principles, and techniques, students complete lab projects that design, document, and implement AI solutions. Lab projects are entirely your own individual work. They are due the midnight BEFORE the scheduled class meeting and are NOT accepted after the deadline.

Team Project (60%)

The purpose of the project is to investigate and showcase the applicability of symbolic and neural AI approaches. You’ll collaborate with a peer to propose, design, document, and showcase your investigation. Project artifacts include a project proposal, project design, the codebase, presentation, and final report.

The project proposal, design, and first presentation represent 10% of the final grade. Codebase (10%), project report (25%), and final presentation and demo (15%) represent 50% of the final grade.

ASSESSMENT OVERVIEW

Evaluation of your work in this course takes into account the following learning activities:

  • Reading Notes (15%)
    • RN feedback (5%)
    • RN final (10%)
  • Lab Projects (25%)
  • Team Project (60%)
    • Proposal, Design, First presenation (equal weights, totaling 10%)
    • Codebase (10%)
    • Report (25%)
    • Final presentation and demo (15%)
  • Bonus for participation (10%)

LEARNING RESOURCES

Course Website

The course website hosted on GitHub has teaching materials and resources that include this syllabus, weekly slides, assignment guidelines, handouts, and other materials. Canvas site has a link to the course website.

Readings and Zotero Library

Guided by the course goals, the readings in this course are collected in a library of scholarly, technical, and news publications and materials. The library resides on the Zotero platform, which is

Wikipedia’s outline ofAI is another source of information into AI approaches and technologies. It is used to contextualize the assigned reading in the course.

Communication and Collaboration Tools

Because of the highly collaborative nature of the course, we’ll be using a variety of online tools that support collaboration, sharing, and openness.

  • Canvas site hosts announcements, assignments (descriptions and schedule), student submissions and feedback, and student grades.
  • Course Website is hosted on GitHub.
  • GitHub platform is used for student submission of their work products and collaboration on the team project. Using their UNH affiliation, students have free access to the GitHub Student Development Pack.
  • Zoom tool in Canvas is used for online class meetings if remote instruction is needed.
  • Zotero at https://www.zotero.org/groups/4413902/practical_ai for managing bibliographic references used for assigned reading.

All online communication on personal matters between a student and instructor is exclusively using Canvas Inbox tool.

Development Tools and Services

Your laptop is the development platform for learning activities in this class. You can use the operating system you are more comfortable with: Windows, Mac OSX, or a Linux distribution, such as Ubuntu.

The development tools you need to have on your machine installed at the global/system level are:

  • bash shell
  • Python 3 (3.10 or higher)
  • git
  • Visual Studio Code
  • VS Code tools (extensions)
    • GitHub Copilot
    • Jupyter Notebook
    • Debugger
    • Static analysis tools (e.g., pycodestyle, pylint).

Cloud services for which you need create accounts are:

  • AWS SageMaker Studio Lab
  • Google Colab

Use your UNH email address to create accounts on these platforms.

Tech Consultants and Classroom Assistants

The Computing Program has student tech consultants (TCs) and classroom assistants (CAs) who are available to help with your learning activities and any technical aspects in this course.

Center for Academic Enrichment (CAE) Tutoring Services

The Center for Academic Enrichment (CAE) professionals and peers are available to support all UNH Manchester students in maximizing their learning potential through individual in-person and online tutoring, in-class workshops, and study groups in math, writing, course content, study skills, time management, and personal statements. All students registered for UNH Manchester courses are entitled to one hour of individual tutoring, per course, per week. Appointments are available at https://caetutor.unh.edu. For more information contact the CAE at 603 641 4113 or unhm.cae@unh.edu.

CAE tutors are well-prepared to assist with questions, lab and homework assignments, and Python programming. Please make use of one-on-one tutoring sessions.

Use of Other Tools

No phone use is permitted in class, unless directed to do so. For example, you may need to use your phone to login on Canvas.

COURSE SCHEDULE

See separate document (web page) with this information.

COURSE-SPECIFIC POLICIES

Course policies include:

  • Academic integrity
  • Attendance
  • Course workload and credit hour
  • Curtailed operations
  • Early alerts report
  • Late submissions
  • Student accessibility services
  • Temporary academic supports for extended absences with letter

See separate document (web page) that details these policies.

UNIVERSITY-BASED POLICIES AND RESOURCES

These policies and resources include:

  • Confidentiality and mandatory reporting
  • Crisis assessment and risk evaluation (CARE) team
  • Emotional or mental health distress
  • Financial literacy resources
  • Food pantry
  • Library
  • QPR
  • Sexual harassment and rape prevention (SHARPP)

See separate document (web page) that details these policies and resources.