NLP Session 1: Convexity and Unconstrained Optimization
Dear students,
The NLP Session 1 learning package is now available. This lesson covers convex and nonconvex problems, gradient descent, step-size selection, and Newton methods.
Please allow approximately three hours. Watch the narrated slides or the four video chapters, pause to work through the examples, run the Python lab, and complete the worksheet before checking the solutions. Then submit the NLP Exit Ticket and feedback using the links below. The videos alone take about 41 minutes; the remaining time is for practice and the lab.
Narrated slides: https://www.skhgroup.net/teaching/2105623/nlp-session-1/slides.html
Video chapters: https://www.skhgroup.net/teaching/2105623/nlp-session-1/video.html
PowerPoint with narration: https://www.skhgroup.net/teaching/2105623/nlp-session-1/NLP1-slides-narrated.pptx
Slide PDF: https://www.skhgroup.net/teaching/2105623/nlp-session-1/NLP1-slides.pdf
Python lab: https://www.skhgroup.net/teaching/2105623/nlp-session-1/lab/index.html
Practice worksheet: https://www.skhgroup.net/teaching/2105623/nlp-session-1/NLP1-exercises.pdf
Worked solutions: https://www.skhgroup.net/teaching/2105623/nlp-session-1/NLP1-solutions.pdf
Online Exit Ticket: https://optimization-2105623-class-check.soorathep-k.chatgpt.site/nlp-session-1
Feedback: https://optimization-2105623-class-check.soorathep-k.chatgpt.site/nlp-session-1/feedback
The Exit Ticket includes five multiple-choice questions with immediate explanations and one written response. No name or email is collected. In your feedback, please identify any slide, calculation, or lab step that remains unclear.
Please attempt each problem before reading its solution. Thank you.