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AKTU Introduction to Machine Learning in Pharmaceutical Sciences (Theory) (BP301T) Syllabus PDF – B.Pharm 3rd Semester
Introduction to Machine Learning in Pharmaceutical Sciences (Theory) BP301T
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Official AKTU syllabus of Introduction to Machine Learning in Pharmaceutical Sciences (Theory) (BP301T) for B.Pharm 3rd Semester – 2 credits, L-T-P 2-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Introduction to Machine Learning in Pharmaceutical Sciences (Theory)
Download PDFBP301T · 2 credits · L-T-P 2-0-0
Course Objectives
- Introduce the fundamental concepts of Artificial Intelligence, Machine Learning, and Data Science and their relevance to pharmaceutical sciences.
- Develop conceptual understanding of machine learning workflows, including model building, training testing strategies, and evaluation.
- Explain the principles and applications of regression models for predicting continuous outcomes in pharmaceutical and healthcare data.
- Familiarize students with classification and tree-based models used for clinical prediction and decision-making.
- Introduce unsupervised learning techniques and demonstrate their applications in pattern recognition and healthcare data analysis.
Course Outcomes
| CO | Upon successful completion of this course, the students will be able to: |
| No. | |
| 1 | Explain the basic concepts of artificial intelligence and machine learning, including types of learning and model development. |
| 2 | Apply regression models to analyze pharmaceutical data and interpret model outputs and performance metrics. |
| 3 | Analyze classification models for predicting categorical clinical outcomes using evaluation measures such as accuracy, sensitivity, and ROC AUC. |
| 4 | Interpret tree-based and ensemble learning models for decision-making in pharmaceutical and healthcare applications. |
| 5 | Utilize unsupervised learning techniques such as clustering to identify patterns in healthcare and pharmaceutical datasets. |
Detailed Syllabus:-
| Unit No. | Topics Lectures | No. of |
| I | 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 | 6 Hours |
| II | 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 | 6 Hours |
| III | 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. | 6 Hours |
| IV | 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. | 6 hours |
| V | 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. | 6 Hours |
Recommended References
- James, G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning with Applications in Python. Springer. Available at: statlearning.com.
- Simon, G. J. and Aliferis, C. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Springer.
- Brown, N. Artificial Intelligence in Drug Discovery. Royal Society of Chemistry.
- Walters, S. J., Campbell, M. J. and Machin, D. Medical Statistics: A Textbook for the Health Sciences. Wiley-Blackwell.
- Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow.
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