EN813 711 Optimization: Hard, Soft, and Emerging Approaches

Semester 1/2567 (Jun-Oct 2024)

Course description

(Alternative title: Computation, Optimization, and Intellectual Exploration.) The class discusses computational science and engineering, including numerical and combinatorial optimization, as well as applications and/or a selected topic. Students are expected to actively participate in class and assignments.

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Instructor

• Tatpong Katanyukul

Textbooks

• Novak, Numerical Methods for Scientific Computing, Equal Share Press 2022
• Chong and Zak, Introduction to Optimization, Wiley 2013
• Russell and Norvig, Artificial Intelligence: Modern Approach, Pearson 2021
• Yanofsky and Mannucci, Quantum Computing for Computer Scientists, Cambridge 2008

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Assessments (tentative)

• Participation: 10%
• Assignments/Projects/Final Evaluation: 50%
• Exercises/Homeworks: 40%
  • autolab.en.kku.ac.th

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Main materials

• Orientation and Overview.
• Numerical Computation and Visualization.
•   • Hands-on: Numpy (colab)
•   • Hands-on: Matplotlib (colab)
• Introduction to Search and Optimization.
•   • Introduction to Artificial Intelligence
•   • Optimization
•     • Hands-on: optimization (colab)
•     • Hands-on: gradient descend algorithm (colab)
•   • Search
•     • Hands-on: search with aux (colab)
•   • Heuristic search
•     • Hands-on: heuristic search (colab)
•   • Local search
•     • Hands-on: local search with aux (colab)
• Intellectual Exploration/Emerging Approach
•   • Possible options / past classes

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Learn more

If you are interested in the development and state-of-the-art in the field, here's one good place to start with:

• Papers with Code
• Kaggle
• Latest development
  ** Conference on Neural Information Processing Systems (NeurIPs)
  ** International Conference on Learning Representations (ICLR)

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Miscellaneous

COE Wiki

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Academic Honesty

You are expected to do your own work to show understanding, skills, and what you have learned. All submitted works (including HOMEWORKS!) should be your own and ACADEMIC DISHONESTY IS NOT ALLOWED.
Academic dishonesty includes:

• Copying words, ideas, codes, or other materials from another source without giving credit to the original author;
• Copying from your peers or seniors, and then submitting the work as your own;
• Employing or letting another person to alter, revise, or edit your work, and then submitting the work as your own;
• Intentionally letting any of your peers to copy your work and submit as one's own;
• Submitting work automatically produced by an emerging tool (e.g., AI) as your work.

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Last updated 2024 Jan 7th.