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Semester III · PCI syllabus 2026
Introduction to Machine Learning in Pharmaceutical Sciences
BP301T
2 credits
Theory
Coming soon
Unit 1: Foundations of Machine Learning
- Definition and scope of Artificial Intelligence, Machine Learning, and Data Science
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Features and labels, training data and testing data
- Concept of model building and prediction
- Train-test split, overfitting and underfitting
- Conceptual explanation of bias-variance trade-off
- Overview of the basic ML workflow
Unit 2: Regression Models in Healthcare
- Overview of the concepts in predictive modeling for continuous outcomes, with emphasis on interpretation of outputs, and applications in pharmaceutical sciences
- Linear regression and multiple linear regression
- Model coefficients and their interpretation
- Residuals and goodness-of-fit
- Performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² score
- Implementation of regression models on pharmaceutical data ( e.g. dose–response relationships, predicting drug dissolution rates, estimating PK parameters), and demonstration of predictive modeling using python libraries such as sklearn
Unit 3: Classification Models in Clinical Applications
- Overview of the following ML models used for categorical prediction, with emphasis on conceptual understanding, interpretation of outputs and applications in pharmaceutical sciences
- Logistic regression
- Probability output and threshold selection
- Confusion matrix
- Accuracy, sensitivity, specificity, precision, recall
- Intuitive understanding of ROC curve and AUC, and k- Nearest Neighbors
- Demonstration of predictive modeling using pharmaceutical and clinical examples such as predicting adverse drug reactions, disease risk prediction, binary therapeutic outcome modeling
Unit 4: Tree-Based Models & Ensemble Learning
- Overview of the following ML models with emphasis on intuitive and conceptual understanding
- Decision Trees (structure and splitting criteria), interpretation of decision paths and feature importance
- Random Forest, overview of ensemble concept
- Advantages and limitations of tree-based models
- Demonstration of these models for pharmaceutical and clinical applications such as ADR risk stratification, Patient classification, Predicting treatment outcomes etc
Unit 5: Unsupervised Learning & Practical Case Studies
- Overview of the unsupervised learning with emphasis on intuitive and conceptual understanding of pattern recognition and clustering, and their applications in pharmaceutical sciences
- Concept of unsupervised learning
- Clustering overview, K-means clustering, choosing number of clusters, Interpreting cluster outputs
- Mini-case study integrating regression, classification, or clustering on healthcare datasets to demonstrate Patient segmentation, and drug grouping based on properties
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