CIS1861 - ARTIFICIAL INTELLIGENCE PROGRAMMING
3 Credit Hours
Student Level:
This course is open to students on the college level in either the Freshman or Sophomore year.
Catalog Description:
CIS1861 - Artificial Intelligence Programming (3 hrs.)
This course explores AI principles, tools, and applications, covering machine learning, deep learning, and real-world AI implementations. Students will develop, evaluate, and apply AI models in areas like image recognition, natural language processing, and automation. Emphasis is placed on critical thinking, problem-solving, and ethical considerations to prepare students for AI innovation in academia, industry, and research.
KRSN: n/a
Course Classification: Lecture
Prerequisites:
None
Co-requisites:
None
Controlling Purpose:
This course provides students with a comprehensive understanding of Artificial Intelligence (AI), from its foundational principles to advanced applications. Through a structured exploration of AI history, development tools, machine learning, deep learning, and real-world implementations, students will gain both theoretical knowledge and hands-on experience. The course emphasizes critical thinking, problem-solving, and ethical considerations in AI deployment, preparing learners to develop, evaluate, and apply AI models across diverse domains such as image recognition, natural language processing, data analysis, and AI-driven automation. By the end of the course, students will be equipped with the skills to innovate and responsibly harness AI technologies for future advancements in academia, industry, and research.
Learner Outcomes:
Upon completion of the course, the student will:
- Explain the fundamental concepts and historical evolution of Artificial Intelligence (AI), including key milestones, AI hype cycles, and the current state of AI development.
- Utilize AI development tools, including hardware, software, and programming frameworks such as Python, to implement AI models and analyze datasets.
- Apply machine learning and deep learning techniques, including supervised, unsupervised, and reinforcement learning, to train and evaluate AI models for various applications.
- Develop AI-based solutions for real-world applications, such as image classification, object detection, face recognition, natural language processing, and data analysis.
- Evaluate ethical considerations, biases, and challenges in AI deployment, ensuring responsible and fair use of AI in different sectors.
- Implement AI computing techniques, including cloud AI, edge AI, GPU/TPU acceleration, and quantum AI, to optimize performance and scalability in AI-driven applications.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content not including the final examination period. Instructors may add other material as time allows.
UNIT 1: Introduction to Artificial Intelligence
Outcomes: Upon completion of this unit, students will be able to:
- Explain the fundamental concepts of Artificial Intelligence (AI).
- Describe the historical evolution of AI, including key milestones and AI hype cycles.
- Assess the current state of AI development and its impact on various industries.
UNIT 2: AI Development Tools and Frameworks
Outcomes: Upon completion of this unit, students will be able to:
- Identify and utilize key AI development tools, including hardware and software frameworks.
- Implement AI models using Python and related libraries such as TensorFlow and PyTorch.
- Analyze datasets using AI programming frameworks to extract insights and patterns.
UNIT 3: Machine Learning and Deep Learning Techniques
Outcomes: Upon completion of this unit, students will be able to:
- Apply supervised, unsupervised, and reinforcement learning techniques to AI model training.
- Train and evaluate machine learning models for various applications.
- Implement deep learning architectures such as convolutional and recurrent neural networks.
UNIT 4: AI-Based Solutions for Real-World Applications
Outcomes: Upon completion of this unit, students will be able to:
- Develop AI-driven applications for tasks such as image classification, object detection, and face recognition.
- Apply natural language processing (NLP) techniques to analyze and interpret human language.
- Utilize AI for data analysis and decision-making in real-world scenarios.
UNIT 5: Ethical Considerations and Challenges in AI Deployment
Outcomes: Upon completion of this unit, students will be able to:
- Evaluate ethical concerns and biases in AI development and deployment.
- Assess the impact of AI on society, including fairness, accountability, and transparency.
- Propose strategies to ensure responsible and fair AI usage in different sectors.
UNIT 6: AI Computing Techniques and Optimization
Outcomes: Upon completion of this unit, students will be able to:
- Implement AI computing techniques, including cloud AI, edge AI, and GPU/TPU acceleration.
- Optimize AI model performance and scalability for various applications.
- Explore emerging AI computing paradigms, such as quantum AI, for advanced problem-solving.
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
None
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies:
402.00 Academic Code of Conduct
263.00 Student Appeal of Course Grades
403.00 Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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