Fundamentals of Artificial Intelligence (AI)

Overview

Course description

This course provides a foundational knowledge of Artificial Intelligence (AI) technologies, introducing students to Large Language Models (LLMs), prompt engineering, the design landscape, cybersecurity threats, and ethical considerations of Generative AI applications. The course also introduces students to AI-augmented software, systems, and applications development.

Course content

Programming Foundations for AI Development using high-level programming languages such as Python
AI Concepts and Large Language Models, including model architecture, pre-training, fine-tuning, generative AI application types (language modelling, question answering, code generation, content creation, logical reasoning), and LLM limitations (bias, hallucination, sustainability, output control)
Prompt Engineering, including zero-shot, few-shot, and chain-of-thought techniques, and prompt evaluation across generative AI tasks
Applied AI Development, including production-ready environment setup, pre-trained model deployment using Docker, GitHub, LangChain, and Hugging Face, and Retrieval-Augmented Generation (RAG) pipeline implementation
Cybersecurity Considerations for AI, including threat identification, mitigation, robustness, and data protection
Ethics and Responsible AI, including ethical risks, privacy, intellectual property, and compliant AI usage
AI-augmented System Design and Development, including integration of generative models into software workflows and end-to-end application development from requirements to deployment

Learning activities

In this course, students participate in a variety of learning activities such as lecture demonstrations, in-class exercises, in-class discussions, hands-on group or individual projects, and background research activities.

Means of assessment

This is a letter-graded course. Assessment will be based on course objectives and will be in accordance with the Douglas College Evaluation Policy.

Labs/Assignments	10-15%
Project w/ Presentation*	20-25%
Midterm Examination**	30-35%
Final Examination**	30-35%
Total	100%

* Projects may involve group work. Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with college policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.

** Practical hands-on computer programming exam. In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components.

Learning outcomes

Upon successful completion of this course, the student will be able to:

Apply programming fundamentals in a high-level language such as Python to develop AI-powered programs and solutions.
Analyze the architecture, pre-training, fine-tuning, application types, and limitations of Large Language Models (LLMs) including bias, hallucination, and challenges in controlling model output.
Evaluate prompting strategies including zero-shot, few-shot, and chain-of-thought techniques across varied generative AI tasks.
Implement a Retrieval-Augmented Generation (RAG) pipeline within a production-ready development environment using tools such as Docker, GitHub, LangChain, and Hugging Face.
Assess cybersecurity threats, ethical risks, privacy concerns, and intellectual property implications in generative AI systems using industry standards and responsible AI principles.
Design an AI-augmented application that integrates a generative model into a functional software workflow from requirements to deployment.

Textbook materials

Dhamani, N., & Engler, M. Introduction to generative AI. Manning Publications. Latest Edition

Rodriguez, C. Generative AI foundations in Python: Discover key techniques and navigate modern challenges in LLMs. Packt Publishing. Latest Edition

Other textbook approved by the department.

Requisites

Prerequisites

Minimum Grade C in CSIS 1175

Corequisites

None

Equivalencies

None

Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

May 2027 (Current)

Course Transfers to Other Institutions

Below are current transfer agreements from Douglas College to other institutions for the current course guidelines only. For a full list of transfer details and archived courses, please see the BC Transfer Guide.

Institution	Transfer details for CSIS 2390
There are no applicable transfer credits for this course.