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Course Planning by Program

2024-25

Essential Objectives

Course Syllabus


Revision Date: 18-Jan-24
 

Spring 2024 | CIS-1712-VO01 - ST in CIS: Introduction to Artificial Intelligence


Online Class

Online courses take place 100% online via Canvas, without required in-person or Zoom meetings.

Location: Online
Credits: 3 (45 hours)
Day/Times: Meets online
Semester Dates: 01-23-2024 to 05-06-2024
Last day to drop without a grade: 02-11-2024 - Refund Policy
Last day to withdraw (W grade): 03-24-2024 - Refund Policy
This course has started, please contact the offering academic center about registration

Faculty

Amy Moore
View Faculty Credentials
View Faculty Statement
Hiring Coordinator for this course: Deb Grant

Course Description

This course introduces basic concepts and applications of artificial intelligence (AI), including AI project cycles and tools. Students explore and identify issues surrounding AI including ethics, bias, culture, regulations, and professional expectations, as well as the growing impact of AI in different fields and industries. Prior learning in computer science is not required.


Essential Objectives

1. Identify AI and differentiate between automation and AI.
2. Discuss and explain the history, origins, and development of AI including current trends and applications in various industry sectors and career fields.
3. Demonstrate an understanding of how different AI applications and datasets are used in various settings.
4. Identify the different stages of an AI project required to follow industry standards and examine the different roles each member of a typical Data Science team plays in a project.
5. Examine the cultural and social impacts, ethical concerns, and issues of bias and regulation around AI practices now and in the future.
6. Describe basic concepts and models encountered in Machine Learning and Deep Learning.
7. Classify different kinds of data, evaluate the quality of data, and examine different methods of data mining and storage.
8. Examine common no-code tools available for AI project building and develop a use case using no-code tools in each domain of AI.
9. Discuss the future of AI based on emerging technological trends.


Required Technology

More information on general computer and internet recommendations is available on the CCV IT Support page. https://support.ccv.edu/general/computer-recommendations/

Please see CCV's Digital Equity Statement (pg. 45) to learn more about CCV's commitment to supporting all students access the technology they need to successfully finish their courses.


Required Textbooks and Resources


*** This is a no cost textbook or resource class. ***

CIS-1712-VO01 Link to Textbooks/Resources Information for this course in eCampus.

The last day to use a Financial Aid Advance to purchase textbooks/books is the 3rd Tuesday of the semester. See your financial aid counselor at your academic center if you have any questions.


Methods

This course material was developed by Intel and has been taught around the country at many colleges and universities as a synchronous class where students, at a minimum, met via an online video platform for a few hours every week.

While the content in this course is divided into 15 weekly modules, you may do the work each week on your "own time." Some weeks there will be mid-week deadlines. Used your Canvas calendar to track your assignment due dates. There's a lot of sharing and discusion in this course and the timely submission of your work is necessary to share with your classmates as much as this content expects.

There is also a great deal of reading and pre-recorded video since there are no scheduled lectures. We use a mix of discussion forums and then surveys of those forums to share information and the instructor will be a regular participant in getting shared information out to students.

There is a fair bit of writing in this course - individually and in groups. Use your tools to check grammar and spelling and consider using tutor.com or the Learning Center to have someone help you edit your work before submission.

As this is a Special Topics course and the first time it is introduced, the instructor intends to be very involved in your process through this course. The best method for first questions is through the Canvas Messaging system. From there the instructor may schedule group Zoom sessions or make additional videos or both.

The following teaching methods are used (but not restricted to):

  • Reading
  • Videos
  • Writing
  • Research
  • Group research & writing
  • Problem solving cases
  • Discussion forums
  • Surveys
  • Working with free web-based AI tools


Evaluation Criteria

The course grades are broken down into the following categories:

Discussions 25%
Assignments 25%
Projects 20%
Exams 25%
Self-Assessments 5%
Total 100%

Grading Criteria

CCV Letter Grades as outlined in the Evaluation System Policy are assigned according to the following chart:

 HighLow
A+10098
A Less than 9893
A-Less than 9390
B+Less than 9088
B Less than 8883
B-Less than 8380
C+Less than 8078
C Less than 7873
C-Less than 7370
D+Less than 7068
D Less than 6863
D-Less than 6360
FLess than 60 
P10060
NPLess than 600


Weekly Schedule


Week/ModuleTopic  Readings  Assignments
 

1

Demystifying AI & Evolution of AI

  

  1. Students will be able to describe the historical context of data collection and its relationship with AI, including key milestones and breakthroughs. (EO2)

  2. Students will be able to identify the current limitations of AI, such as ethical concerns, bias, and interpretability. (EO1, EO2, EO3, EO5)

  3. Students will be able to evaluate how autonomous vehicles, 5G, big data, and IoT are transforming the AI industry, and provide specific examples of their impact on various domains. (EO2, EO3, EO5, EO9)

  

· Discussion: Introduce yourself

· Discussion: Research AI applications

· Discussion: The Imitation Game[FF1][MD2][MD3]

· Writing: Course Expectations

Exam: on readings and videos

 

2

AI and Technolgy

  

1. Students will be able to describe the historical context of data collection and its relationship with AI, including key milestones and breakthroughs. (EO2)

2. Students will be able to identify the current limitations of AI, such as ethical concerns, bias, and interpretability. (EO1, EO2, EO3, EO5)

3. Students will be able to evaluate how autonomous vehicles, 5G, big data, and IoT are transforming the AI industry, and provide specific examples of their impact on various domains. (EO2, EO3, EO5, EO9)

  

· Quiz: Module 1 Discussion Review

· Assignment: Choose a 5G/IoT/AI industry sector topic to research and present to class

· Assignment: Research topic and gather appropriate media

· Discussion: Share your Topic with the class

Exam: on readings and videos

 

3

Industry 4.0 - Digitalization

  

Students will value the need for digitalization and its connection with the future of AI. They will appreciate AI by acknowledging its application its different sectors of the industry. (EO02, EO03, EO09)

  

· Survey 1: Review and Assess Module 2 Discussion 1

· Assignment 1 – Planet Money Podcast reaction essay

· Group Project 1 – Explore AI in an industry

· Discussion 1: Digitization

Exam: Digitization

 

4

Domains of AI

  

1. Students will be able to differentiate between the different domains of AI, including machine learning, deep learning, natural language processing, and computer vision. (EO6, EO7)

2. Students will be able to describe the characteristics and potential applications of each domain. (EO7)

3. Students will understand the importance of datasets and statistical analysis in AI. (EO6, EO7)

4. Students will be able to identify real-world examples of the different domains of AI and their applications. (EO3)

  

Assignment 1: Make a Computer Vision image

Assignment 2: Explore Natural Language Processing with ChatGPT and Bard

Discussion 1: Share Computer Vision image and process

Discussion 2: Share Data Visualization from RAWGraphs

Discussion 3: Share group project – Explore AI in an Industry

Exam: Domains of AI

 

5

AI Project Cycle

  

Students will examine different stages of the AI Project which can help them execute their AI Project following industry standards. (EO04)

  

Survey to review Computer Vision forum

Survey to review Data Visualization forum

Survey to review group projects on AI in Industry

Assignment: 4Ws Problem Canvas

Exam on AI Project Cycle

 

6

AI Project Cycle cont. & Societal Impact of AI

  

1. Students will be able to understand the different stages of an AI project, including modeling, evaluation, and deployment.

2. Students will be able to identify and select appropriate AI models for different tasks, such as linear regression for regression tasks and decision trees for classification tasks.

3. Students will be able to evaluate the performance of AI models using various metrics, such as accuracy, precision, recall, and F1 score.

4. Students will be able to identify the key considerations for deploying AI models in production, such as hardware requirements and processing power.

5. Students will examine the immediate impacts of current AI practices on society. They will also be able to appreciate the ehtical concerns of AI applications.

  

Assignment 1: Evaluate and Predict Regression Problem

Assignment 2: Evaluate and Predict Regression Problem

Assignment 3: Evaluate and Predict Classification Problem

Assignment 4: Research and write about EU AI Act

Discussion: Research and write about one of 9 AI Ethical concerns

Exam: AI Project Cycle & Societal Impacts of AI

 

7

Elements of ML

  

1. Students will be able to define machine learning and describe how it differs from traditional programming. (EO06)

2. Students will be able to explain the history of machine learning and why it has become an important field in computer science. (EO02)

3. Students will be able to identify several types of machine learning models, including supervised learning, unsupervised learning, and reinforcement learning. (EO06, EO07)

4. Students will be able to analyze real-world applications of machine learning and discuss the potential benefits and challenges associated with this technology. (EO06)

  

Assignment 1: Respond to classmates’ posts from Module 6 discussion AI Ethical concerns

Assignment 2: Evaluate ML scenarios and determine supervised or unsupervised ML

Discussion 1: Reinforcement Learning

Module 7 Exam: Elements of Machine Learning based on videos and reading

 

8

Elements of DL

  

Students will be able to deconstruct the working behind simple Neural Networks (EO06)

Students will be able to outline the difference between some common DL models with the help of examples and applications. (EO06)

  

Module 7 Discussion 1 Review

Working with Generative Adversarial Networks with GANPaint

Neural Networks at work with TensorFlow

Discussion: Deepfakes – benefits, harms, global regulation

Discussion: Explore DL products

Exam: Elements of Deep Learning

 

9

Data – Fuel for AI

  

1. Students will be able to define and explain the concept of data literacy. (EO07)

2. Students will be able to differentiate between structured data and unstructured data. (EO07)

3. Students will be able to identify and explain different methods of data mining. (EO07)

4. Students will be able to identify and explain different methods of data storage. (EO07)

  

Review and respond to Module 8 Discussion 1

Review and respond to Module 8 Discussion 2

Big Data Never Sleeps – Predicting the future on Big Data based on infographics from Domo

KNIME Build your first workflow steps 1-5

KNIME Build your first workflow step 6 (Project)

Discussion: Ethics and Responsibilities of Cloud Storage

Discussion: Big Data Never Sleeps – share work from Assignment

Module 9 Exam

 

10

AI Data Science Teams

  

1. Students will be able to describe the different roles while working on an AI project. (EO04)

Students will also discuss the roles played by different key stakeholders in a typical AI team. (EO04)

  

Assignment: Respond to Module 9 Discussion 1 Ethics and responsibilities of cloud storage

Assignment: Respond to Module 9 Discussion 2 Predicting the future of Big Data based on the last 11 years

Assignment: Which AI/Data Science role do you highly associate with?

Module 10 Exam

 

11

Tools to Implement AI

  

1. Students will be able to identify and describe the different types of tools available for implementing AI, including both no-code and code-based options. (EO08)

2. Students will understand the landscape of no-code AI tools and their potential applications. (EO08)

3. Students will be able to compare and contrast the no-code AI process with the experience using Teachable Machine to create and train their own AI models, and understand its potential applications in various industries. (EO08)

  
 

12

AI Project I – Statistical Data

  

Students will be able to create a No-Code AI solution in the domain of Statistical data. (EO08)

  
 

13

AI Project II – Natural Language Processing

  

Students will be able to create a No-Code AI solution in the domain of Natural Language Processing. (EO08)

  
 

14

AI Project III – Computer Vision

  

Students will be able to create a No-Code AI solution in the domain of Computer Vision (EO08)

  
 

15

Future Possibilities of AI

  

1. Students will learn about the potential impact of Reinforcement Learning and Quantum Computing on the future of AI. (EO09)

2. Students will understand the concepts of Artificial General Intelligence (AGI) and the different types of AI. (EO02, EO09)

3. Students will explore the emerging software and hardware technologies and their direct impact on the development of AI. (EO09)

  
 

Attendance Policy

Regular attendance and participation in classes are essential for success in and are completion requirements for courses at CCV. A student's failure to meet attendance requirements as specified in course descriptions will normally result in a non-satisfactory grade.

  • In general, missing more than 20% of a course due to absences, lateness or early departures may jeopardize a student's ability to earn a satisfactory final grade.
  • Attending an on-ground or synchronous course means a student appeared in the live classroom for at least a meaningful portion of a given class meeting. Attending an online course means a student posted a discussion forum response, completed a quiz or attempted some other academically required activity. Simply viewing a course item or module does not count as attendance.
  • Meeting the minimum attendance requirement for a course does not mean a student has satisfied the academic requirements for participation, which require students to go above and beyond simply attending a portion of the class. Faculty members will individually determine what constitutes participation in each course they teach and explain in their course descriptions how participation factors into a student's final grade.

Accessibility Services for Students with Disabilities:


CCV strives to mitigate barriers to course access for students with documented disabilities. To request accommodations, please
  1. Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/discover-resources/students-with-disabilities/
  2. Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.
  3. Once created, students will share the accommodation plan with faculty. Please note, faculty cannot make disability accommodations outside of this process.


Academic Integrity


CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.