Details of CS5103 (Autumn 2025)
| Level: 5 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| CS5103 | Applied Machine Learning | Kripabandhu Ghosh, Saptarshi Pyne |
| Syllabus |
|---|
| Learning to use Python machine learning libraries
o Writing and importing code in Python o Learning to use the scikit-learn and TensorFlow libraries Conducting classification tasks o A brief review of classification algorithms or classifiers o Recognizing hand-written digits using a support vector classifier o Conducting a comparative study of the widely used classifiers using insilico datasets o Investigating the class boundaries learned by the linear discriminant analysis (LDA) versus that of the quadratic discriminant analysis (QDA) o Predicting whether the share market will move up or down using a naive Bayes classifier and a k-nearest neighbours (KNN) classifier o Identifying the peak hours of bike rentals using linear regression and Poisson regression Performing feature extraction o Extracting features from text documents o Extracting features from image files Selecting linear models (with and without regularization) that best describe a given dataset o A brief review of linear model selection algorithms o Choosing the models that best describe how salaries of baseball players vary based on various statistics associated with the players performance: Using least squares regression, partial least squares (PLS) regression, ridge regression, lasso regression, and principal components regression Utilizing tree-based algorithms for predication o A brief review of tree-based algorithms o Predicting housing prices with different types of decision trees (classification trees, regression trees) and random forests (with and without bagging and boosting) Employing support vector machines (SVMs) for classification tasks o A brief review of SVMs o Employing an SVM for one-class classification o Employing an SVM for two-class classification o Employing an SVM for multi-class classification Clustering o A brief review of clustering algorithms o Subgrouping cancer cell lines into cancer types using the k-means and hierarchical clustering algorithms Training and testing artificial neural networks (ANNs) o A brief review of ANNs o Deploying a single-layer ANN o Deploying a multi-layer ANN Modelling real-world systems with graphical models o A brief review of graphical models o Modelling a stock market using Bayesian networks o Denoising images using Markov random fields (MRFs) o Understanding speech with hidden Markov models (HMMs) Demonstrating a few state-of-the-art applications of machine learning o Application of large language models for legal text summarization o Application of word embedding for information retrieval o Classification of aerial photographs under scarcity of labelled samples o Prediction of 3D protein structures with AlphaFold |
| Prerequisite |
|---|
| CS1101, CS3102 or equivalent |
| References |
|---|
| Textbook
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer Nature. Reference books Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Duda, R. O., & Hart, P. E. (2006). Pattern Classification. John Wiley & Sons. Marsland, S. (2011). Machine Learning: An Algorithmic Perspective. Chapman and Hall/CRC. |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 1 | Not Allowed |
| 2 | IP | 3 | Not Allowed |
| 3 | MP | 1 | Not Allowed |
| 4 | MP | 3 | Not Allowed |
| 5 | MR | 1 | Not Allowed |
| 6 | MR | 3 | Not Allowed |
| 7 | MS | 3 | Not Allowed |
| 8 | MS | 5 | Not Allowed |
| 9 | MS | 7 | Elective |
| 10 | MS | 9 | Elective |
| 11 | RS | 1 | Elective |
| 12 | RS | 2 | Elective |