EN813 706 Artificial Neural Networks

Semester 1/2565

Course description

The class discusses development, motivation, background, state-of-the-arts of Artificial Neural Network, Pattern Recognition, Machine Learning, and related issues, as time allows. Students are expected to actively participate in class and assignments.

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Textbooks

• ธัชพงศ์ กตัญญูกุล การผจญภัยของการเรียนรู้ของเครื่องในโลกการรู้จำรูปแบบ 2023
• C. M. Bishop, Pattern Recognition and Machine Learning. Springer 2007
• Ian Goodfellow et al., Deep Learning. MIT Press 2016

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Main materials

• Orientation, introduction, and overview
• Computation and numerical programming
  • Hands-on: vectorization, numpy, and matplotlib (colab)
  • Hands-on: numerical programming (colab)
• Textbook reading and discussion
• Optimization
  • Hands-on: optimization (colab)
  • Hands-on: gradient descend algorithm (colab)

• Introduction to machine learning
  • Hands-on: curve fitting (colab)

• Generalization
  • Hands-on: overfitting and aux (colab)

• Motivations/perceptrons
• Multi-layer perceptrons
  • Hands-on: mlp (from scratch)
  • Hands-on: sklearn (colab)

• Practical tricks/applications
  • Input normalization; aux file; (colab)
  • Classification (colab)
  • Multiclass classification (colab)
  • Application examples (colab)

• Deep learning
• Selected topics
  • CNN motivation with keras (colab)
  • CNN with keras (colab)
  • Object detection (colab)
  • Object detection: interactive version (colab)

• Paper reading and discussion
• Class project (tentative)

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Additional materials

• Overview to AI
• Agent
  ** Hands-on: sarsa mountain car (R project)
• Search
  ** Hands-on: search (Python)
• Heuristic search
  ** Hands-on: heuristic search (Python)
• Local search
  ** Hands-on: local search (Python)
• Logic
  ** Hands-on: logic (Python)
• Handling uncertainty
  ** Hands-on: handling uncertainty (Python)
• Learning

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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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Old class materials

• ANN 2022

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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 answers or codes;
• Copying words, ideas, codes, or other materials from another source without giving credit to the original author;
• Copying from your peers or seniors;
• 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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