Machine Learning (COMP6577001)

Undergraduate course at BINUS University providing a comprehensive overview of machine learning: analyzing variables and distributions, parameter tuning techniques, and supervised and unsupervised learning methods applied to real datasets.

Sessions

• Sessions 1-2: Introduction to Machine Learning — AI trends, learning problem types, learning techniques
• Sessions 3-4: Data and Regression — exploratory data analysis, preprocessing, linear regression
• Sessions 5-6: Classification — logistic regression, model selection and regularization
• Sessions 7-8: Probabilistic and Recommendation Methods — naive Bayes, recommender systems
• Sessions 9-10: Trees and Margins — decision trees and ensemble learning, support vector machines
• Sessions 11-12: Unsupervised Learning — clustering, dimensionality reduction
• Session 13: Machine Learning Operations and Review

Learning Outcomes

• Explain the fundamentals of machine learning concepts
• Interpret the distribution of a dataset using regression methods
• Experiment with classification and clustering algorithms on a given dataset

Resources

• Textbook: James, Witten, Hastie, Tibshirani, and Taylor (2023). An Introduction to Statistical Learning: with Applications in Python (Springer)
• Supplementary: Geron (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (O’Reilly); Bishop (2006). Pattern Recognition and Machine Learning (Springer)

Teaching strategies include lectures, class discussion, Q&A, group discussion and presentation, and case studies. Full session materials are shared with enrolled students only.