CIS 110 - Introduction to Artificial Intelligence

Description
This introductory course in Artificial Intelligence (AI) provides students with a foundational understanding of AI principles, techniques, and applications. The course is designed to equip students with the knowledge and skills necessary to engage with AI concepts and technologies, fostering critical thinking, problem-solving, and creativity within the context of AI.

Credit Hours: 3
Contact Hours: 3

School: School STEM
Department: Computer Information Systems
Discipline: CIS

Course Review & Revision Year: 2028-2029

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1

General Education Requirement: None
General Education Learner Outcomes (GELO):
NA

Course Learning Outcomes:

Distinguish between what is AI and what is not AI.
Differentiate between automation and AI.
Describe the history of AI, its origin, and the development of AI.
Examine different stages of a typical AI project based on industry standards.
Describe the growth in AI technology by examining the development in the fields of IoT, Big Data, and 5G technology.
Distinguish different applications of AI based on datasets.
Differentiate between basic ML models and understand their applications.
Classify Supervised Learning, Unsupervised Learning, and Reinforcement Learning with the help of examples.
Deconstruct the workings behind simple Neural Networks.
Examine different methods of Data Mining and Data Storage.
Discuss the roles played by different key stakeholders in a typical AI team.
Create a No-Code AI solution in the domains of Natural Language Processing, Statistical Data, and Computer Vision.
Appreciate the ethical concerns of AI applications.

Approved for Online Delivery?: Yes

Course Outline:

Demystifying AI and the Evolution of AI
1. Course Overview & Orientation 
2. What is AI? What is not AI?
3. Introduction to Generative AI
4. Demystifying AI – Automation vs AI
Emerging Technology in AI/ Emerging Trends
1. AI-powered Autonomous Vehicles
2. AI Powered by Big Data, IoT, and 5G technology
3. Impact of Generative AI on Trending Technologies
Industry 4.0 - Digitalization
1. What is Digitalization and Its Importance
2. AI in industry – Application of AI in Manufacturing, Healthcare, Transportation, Agriculture, Energy
3. Generative AI in Various industries
Domains of AI
1. What are the three domains of AI?
2. Application of AI in Each Domain
AI Project Cycle
1. What is the AI project cycle? Why is it important?
2. What are the different stages of the AI Project Cycle?
3. Generative AI in Different Stages of the AI Project Cycle
4. Evaluation Metrics for Generative AI Models
Societal Impact of AI
1. What is the ethical consideration while working with AI?
2. What is data privacy?
3. How can AI become inclusive and eliminate bias?
4. How can AI help build sustainable solutions and solve global problems?
5. Ethical Concerns of Generative AI
6. Bias in Generative AI models
Elements of Machine Learning
1. What is the difference between Machine Learning (ML) and Deep Learning (DL)?
2. What are the different Machine Learning algorithms and their applications?
3. Generative Models in ML Algorithms
Elements of DL
1. What is a Neural Network, and what is the inspiration behind developing them?
2. What are some common DL models and their applications?
3. Overview of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Tools to implement AI
1. No-Code tools vs Coding tools
2. What are some popular No-Code and Code based AI tools, their benefits, some examples, and comparisons
3. Generative AI in Enhancing Low-code and No-code Development
4. Low Code + Gen AI = No Code
5. Orientation to Jupyter Notebook
AI Projects (2 to 3 weeks of Projects)
1. Statistical Project
  1. No-Code Tools Specific to Statistical Data
2. Natural Language Processing (NLP) Project
  1. What are the No-Code tools specific to Natural Language Processing (NLP)?
  2. How to implement different No-Code NLP solutions?
  3. Hugging Face Transformers for Generative AI
  4. OpenGPT3 Playground
3. Computer Vision
  1. What are the No-Code tools specific to CV?
  2. Image Synthesis with Generative Models (CycleGAN)
  3. Video and Image Generation with Generative Model - RunwayML
  4. How to implement different No-Code CV solutions?
Future Possibilities of AI
1. What is Quantum computing, and how can it change AI?
2. Hardware Acceleration for Generative AI
3. What is AGI? How long will it take AI to achieve it?

Mandatory CLO Competency Assessment Measures:
None

Name of Industry Recognize Credentials: None

Instructional Strategies:
Lecture 40%-50%

Facilitated Discussion 20%-30%

Facilitated Group Work 40%-50%

Mandatory Course Components:
None

Academic Program Prerequisite: None
Prerequisites/Other Requirements: None

English Prerequisite(s): None
Math Prerequisite(s): Eligible for Math 105 or Higher; SAT Math Score of 24.5 or Higher
Course Corerequisite(s): None
Course-Specific Placement Test: None
Course Aligned with IRW: NA
Consent to Enroll in Course: No Department Consent Required

Total Lecture Hours Per Week: 3
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), 18 graduate credit hours in discipline being taught (HLC Requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details: 18 graduate credit hours in discipline being taught (HLC requirement) - The instructor must possess a minimum of a Master of Computer Science or a Master in Computer Information Systems with demonstrated studies/work with Artificial Intelligence, Machine Learning, or Advanced Algorithms. Professionally qualified through work experience in field - Two years work experience in tech industry directly related to AI, Machine Learning, or Data Science. Program Accreditation Requirement - The instructor must have a Lead Facilitator Certificate in AI for Workforce Program or equivalent. Bachelor's plus 2000 hours of work experience.

General Room Request: ATC CIS Rooms or AI Incubator

Maximum Course Enrollment: 24
Equivalent Courses: None
Dual Enrollment Allowed?: Yes
Number of Times Course can be taken for credit: 1
First Term Valid: Fall 2024 (8/1/2024)
Programs Where This Courses is a Requirement:
Computer Programming, A.A.A.S., Computer Support Specialist, A.A.A.S., Pathway Degree with Computer Information Systems Concentration, A.A., Pre-Computer Science, A.S. (General Transfer)
1st Catalog Year: 2024-2025
People Soft Course ID Number: 105263
Course CIP Code: 11.9999

Name of Course Author:
Jonnathan Resendiz