ITE 301: Introduction to AI and Generative AI

Course Description

This course introduces students to fundamental principles, strategies, and practices necessary for working with and developing generative artificial intelligence (AI). Topics include generative AI models, prompt engineering, neural networks, and large language models. Students examine the use of generative AI in society, ethical issues related to generative AI, and implement AI models to solve problems in the domains of natural language processing and machine learning. (3 credits)

Prerequisites

  • ENG 101: English Composition 1
  • ENG 102: English Composition 2
  • ITE 115: Program Logic and Design with Python

Student Learning Outcomes (SLOs)

Students who successfully complete this course will be able to:

  1. Describe features of generative AI, including its history, fundamental concepts, technological advancements, and future projections.
  2. Describe how generative models function across various industries, focusing on real-world applications and societal impacts.
  3. Describe the role of generative AI in various industries including real-world applications such as image, text, audio, video, and code generation​​.
  4. Describe ethical considerations in generative AI, including key principles like fairness and transparency, and the role of global policy and regulation in AI development.
  5. Describe how natural language processing (NLP) has evolved over time and discuss the role of prompt engineering.
  6. Describe the parameters in prompt engineering and their basic applications and impact on the output of NLP tasks.
  7. Apply prompt engineering skills in NLP tasks, such as text generation, conversation initiation, and simple problem-solving.
  8. Apply prompt engineering skills in image generation.
  9. Describe the various risks associated with prompt engineering, including prompt injection, prompt leaking, and jailbreaking.
  10. Describe the current capabilities and limitations of current AI tools for text, image, video, and audio generation.
  11. Apply AI generation tools to create text, image, video, and audio content.
  12. Discuss the generative AI project lifecycle, how it differs from the conventional AI project lifecycle, and key concepts necessary for generative AI project development such as retrieval-augmented generation (RAG), fine-tuning, pretraining, and evaluation.
  13. Describe the basic structure and function of artificial neural networks and their components and the process of training neural networks.
  14. Describe the basic design principles of transformer neural architectures and large language models (LLM) and discuss the advantages and disadvantages of previous architectures used in text generation.
  15. Describe the fundamentals of diffusion models, their significance in the realm of AI image generation, and how they differ from previous technologies.
  16. Describe the methodologies and technologies utilized in the pre-training phase of LLMs.
  17. Apply fine-tuning methods (instruction-based, single-task, and multi-task) for precise model enhancement.
  18. Describe reinforcement learning from human feedback (RLHF) along with its advantages and limitations.
  19. Describe methods for evaluating AI models using current benchmarks and metrics that measure model performance and quality.
  20. Describe the role and significance of generative AI within the modern technological stack.

Course Activities and Grading

AssignmentsWeight

Discussions (Weeks 1-8)

30%

Midterm Exam (Week 4)

20%

Projects (Weeks 2, 4, 6 & 8)

30%

Final Exam (Week 8)

20%

Total

100%

Required Textbook

  • This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.

Course Schedule

Week

SLOs

Readings and Exercises

Assignments

1

1,2,3,11

Topic: Introduction to Generative AI and Demystifying Generative AI Models

  • Review the lecture material
  • Participate in the Discussions

2

5,6,7,8,9,11

Topic: Introduction to Prompt Engineering

  • Review the lecture material
  • Participate in the Discussions
  • Submit Week 2 Project - Crafting a Narrative with ChatGPT

3

2,3,10,11

Topic: Basics of Text and Image Generation and Using Generative AI for Productivity

  • Review the lecture material
  • Participate in the Discussions

4

2,4,7,8,11,12

Topic: Generative AI Ethics & Generative AI Project Lifecycle

  • Review the lecture material
  • Participate in the Discussions
  • Submit Week 4 Project - Creating an Animated Visual Odyssey
  • Submit Midterm Exam

5

13,14

Topic: Neural Network Fundamentals, Transformers, and the Evolution of Large Language Models

  • Review the lecture material
  • Participate in the Discussions

6

11,15,16

Topic: Diffusion Models, Text to Image Generation, and Pre-Training Generative AI Models

  • Review the lecture material
  • Participate in the Discussions
  • Submit Week 6 Project - Exploring NLP Tasks in Python

7

17,18,19

Topic: Fine-Tuning Generative AI Models, RLHF, Evaluating, and Optimizing Generative AI Models

  • Review the lecture material
  • Participate in the Discussions

8

20

Topic: Prototyping Generative AI Applications

  • Review the lecture material
  • Participate in the Discussions
  • Submit Week 8 Project - Implement AI Tutors using Dolly 3B
  • Submit Final Exam
  • Complete Course Evaluation

COSC Accessibility Statement

Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.

COSC Policies, Course Policies, Academic Support Services and Resources

Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.