ai

Course map

1. Course Learning Objectives (CLOs) Mapping

Each module in the course supports at least one of these CLOs.

1. Explain key AI concepts and their applications in engineering
2. Use a programming language and AI tools to analyze big data
3. Share data and findings using notebooks and Git for reproducible workflow
4. Apply supervised and unsupervised learning techniques for predictive modeling
5. Employ generative AI to access and build upon specialized knowledge with critical oversight
6. Identify and evaluate ethical and professional considerations of AI
7. Develop a mindset required to lead and innovate responsibly in a future defined by human-AI collaboration

2. Modules

Module 0: Introduction

Learning objectives: Describe the role and applications of AI in engineering practice and research.

• CLOs Supported: Introduction to AI in Engineering (CLO 1)
• Assessment: Homework 1 – Introduction to AI in Engineering

Module 1: Start Coding

Learning objectives: Install and use a dynamic programming language (Python), notebooks (JupyterLab), and version control (GitHub)

• CLOs Supported: Installing and Using Python and JupyterLab, Review of Programming Fundamentals (CLOs 2,3)
• Assessment: Homework 2 – Using Python, JupyterLab and GitHub, Homework 3 – Fundamentals of Python Programming

Module 2: Coding with AI

Learning objectives: Use AI coding assistants and prompt engineering for effective and efficient coding

• CLOs Supported: AI Coding Assistants, Prompt Engineering (CLO 2)
• Assessment: Homework 4 – Using AI Coding Assistants for Programming Tasks, Homework 5 – Prompt Engineering for Exploratory Data Analysis

Module 3: Supervised Learning

To be completed

3. Participation

Each module includes an exit ticket discussion post as part of the participation grade, helping students stay engaged with the course and their peers. Exit tickets are brief, end-of-lesson feedback designed to quickly gauge student understanding, questions, provide instructors with immediate feedback, and start discussion. These ensure student engagement with course materials and active learning.

4. Assessment

Assessment in this course utilizes weekly homework assignments designed to reinforce learning objectives across all modules, ranging from foundational concepts to authenthic learning. Critical thinking exercises and practical exercises that mirror the homework assignments are incorporated to promote deeper engagement and skill development.

5. Evaluation

The Feedback and Learning Assessment Survey is conducted after each module to evaluate student learning, assess the difficulty and pacing of the content, and gather feedback for course improvements. This survey encourages self-reflection and helps the instructor refine instructional materials, ensuring a well-balanced and engaging learning experience.

6. Course Alignment Table

CLOs	Module	Assessments	Instructional Materials	Learning Activities	Tools
CLO 1	Module 1-7	Exam	Slides, videos, Python codes	Worked examples, practical applications	Python, JupyterLab, Git, ML, DL, GenAI
CLO 2	Module 1-2	HW1-HW5	Slides, videos, Python codes	Worked examples, practical applications	Python, JupyterLab, Jupyter AI
CLO 3	Module 1	HW2	Slides, videos, Python codes	Worked examples, practical applications	Python, JupyterLab, GitHub
CLO 4	Module 3-5	HW6-HW11	Slides, videos, Python codes	Worked examples, practical applications	Python, ML, DL
CLO 5	Module 6	HW12-HW14	Slides, videos, Python codes	Worked examples, practical applications	Python, GenAI
CLO 6	Module 7	HW14	Slides, videos, Python codes	Worked examples, practical applications	Python, GenAI
CLO 7	Module 8	HW15	Slides, videos, Python codes	Worked examples, practical applications	Python, JupyterLab, GitHub

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