| Bahan Kajian | - Machine learning methods: basic concepts (types of machine learning systems, main challenges, development cycle), classification (binary classifier training, performance measures, multi-class/label/output classification, error analysis), linear and logistic regression, linear and non-linear SVMs, decision trees, ensemble learning (voting, bagging & boosting),random forest, dimensionality reduction (curse of dimensionality, main approaches, PCA), clustering (k-means, DBScan, GMM), biomedical examples
- Experiment & survey design: interrelation between data quality, model quality, and model performance
- Machine learning model development: hands-on experience (feature extraction and visualization, feature preprocessing and selection)
- Procedural testing & validation: hands-on experience (model training-testing-validation-evaluation)
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| Capaian Pembelajaran Mata Kuliah (CPMK) | - Menganalisis dan menginterpretasikan hasil pengukuran kinerja sebuah model pembelajaran mesin dengan tepat (IK 6.2, C4)
- Mengimplementasikan model maupun prosedur pembelajaran mesin menggunakan fungsi-fungsi bawaan yang tersedia maupun fungsi baru yang dibuat sendiri (IK 7.2, P3)
- Merancang, melatih, dan memvalidasi model pembelajaran mesin untuk data biomedis dengan metodologi yang tepat dan runtut (IK 8.3, C3)
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