M. Nabil Mahmoud
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ECE 377

Course Description

This course focuses on the engineering applications of Artificial Intelligence (AI), with a particular emphasis on machine learning and deep learning techniques. Students will explore how AI has revolutionized various engineering fields, including computer vision, natural language processing, robotics, and autonomous systems. The course objective is to equip students with practical skills in applying AI to solve real-world engineering problems using contemporary open-source tools. Starting with fundamental AI concepts, the course progresses to advanced topics, ensuring a strong grasp of both theory and implementation. Students will gain hands-on experience in developing AI solutions for engineering challenges.

Learning Outcomes

  1. Understand fundamental AI concepts and their applications in engineering.

  2. Apply machine learning techniques to solve real-world engineering problems.

  3. Develop proficiency in using popular AI frameworks and tools.

  4. Design and implement neural networks for various engineering tasks.

  5. Analyze and interpret results from AI models in engineering contexts.

  6. Evaluate the performance and limitations of AI systems in engineering applications.

  7. Understand ethical considerations and responsible AI deployment in engineering.

  8. Collaborate effectively on AI projects in multidisciplinary engineering teams.

  9. Stay current with emerging AI technologies and their potential impact on engineering fields.

  10. Communicate AI concepts and results clearly to both technical and non-technical audiences.

Textbooks

  • Lecture Notes

Schedule

Date Topic - Slides Homework
Week 1 Python Introduction On Blackboard
Week 2 Maximization and Optimization On Blackboard
Week 3 Linear Regression On Blackboard
Week 4 Polynomial Regression On Blackboard
Week 5 Perceptron and Logistic Regression On Blackboard
Week 6 Support Vector Machines On Blackboard
Week 7 Performance Metrics On Blackboard

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