This course, Advanced Machine Learning and Artificial Intelligence (32-06-1705), provides a comprehensive study of modern ML and AI techniques. Students will gain both theoretical understanding and practical skills in supervised and unsupervised learning, deep learning architectures (CNNs, RNNs, Transformers), fuzzy logic, explainable AI, and emerging generative AI models. The course emphasizes critical analysis, responsible use of AI, and application to real-world case studies in domains such as natural language processing, image recognition, and intelligent decision systems.
Learning is structured around hands-on labs and projects using Docker, JupyterLab, Scikit-Learn, TensorFlow, Keras, PyTorch, and NLP frameworks such as spaCy and PyThaiNLP. Students will develop the ability to evaluate models with advanced metrics, interpret results responsibly, and propose innovative AI-driven solutions.
By the end of the course, learners will:
Understand the concepts, theories, and principles of ML and AI.
Analyze and apply algorithms to diverse datasets.
Demonstrate skills in model evaluation and interpretability.
Apply creativity and ethics in AI-driven problem-solving.
This course is suitable for advanced undergraduate and graduate students, as well as professionals in computer science, data science, and related fields seeking to deepen their AI expertise.
Prior knowledge of Python programming (basic to intermediate).
Familiarity with linear algebra, probability, and statistics.
Experience with introductory machine learning is recommended but not mandatory.
Willingness to engage in hands-on labs using open-source tools and Docker.
Assoc. Prof. Dr. Phayung Meesad
Director, KMUTNB Central Library
Specialist in Machine Learning, Data Analytics, and Smart Digital Libraries.
Dr. Phayung has extensive experience in teaching, research, and leadership in AI and data science, with numerous publications in top journals and conferences.
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How is the course assessed?
Assessment is based on quizzes, lab reports, group projects, a midterm exam, and a final exam.
Can I audit this course without doing assignments?
Yes. Auditing learners can access all lecture materials and labs but will not receive graded evaluations or a certificate.