222TEC001 : Foundations of Data Science (2025)

Instructors

Prof. Viji R. and Jerrin Thomas Panachakel

Online Meeting Link

https://meet.google.com/pmp-dybr-mzb 

Syllabus

• Module 1: Basics of Machine Learning (8 hours)

• Basics of machine learning, supervised and unsupervised learning, examples

• Features, feature vector, training set, target vector, test set

• Over-fitting, curse of dimensionality

• Evaluation and model selection: ROC curves, evaluation measures

• Validation set, bias-variance trade-off

• Confusion matrix, recall, precision, accuracy

• Module 2: Regression and Classification (7 hours)

• Regression: linear regression, error functions in regression

• Multivariate regression, regression applications, bias and variance

• Classification: Bayes’ decision theory

• Discriminant functions and decision surfaces

• Bayesian classification for normal distributions, classification applications

• Module 3: Algorithms and Nonlinear Classifiers (7 hours)

  • Linear discriminant-based algorithm: perceptron, perceptron algorithm

  • Support vector machines

  • Nonlinear classifiers, the XOR problem

  • Multilayer perceptrons

  • Backpropagation algorithm

• Module 4: Unsupervised Learning and Ensemble Methods (8 hours)

  • Unsupervised learning

  • Clustering, examples, criterion functions for clustering

  • Proximity measures, algorithms for clustering

  • Ensemble methods: boosting, bagging

  • Basics of decision trees, random forest, examples

• Module 5: Deep Learning Networks (7 hours)

  • Introduction to deep learning networks

  • Deep feedforward networks

  • Basics of convolutional neural networks (CNN)

  • CNN basic structure, Hyper-parameter tuning, Regularization - Dropouts

  • Initialization, CNN examplesassification, recognition and segmentation, speech recognition, automatic language translation and auto corrections, recommendation engines.

References

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.

2. Theodoridis, S., & Koutroumbas, K. (2003). Pattern Recognition. San Diego: Academic Press.

3. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning. Springer.

4. Duda, R. O., Hart, P. E., & Stork, D. G. Pattern Classification. New York: Wiley.

5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

CO-PO Mapping and Rubrics for Evaluation 

CO-PO Mapping

Evaluation Pattern

• Continuous Internal Evaluation: 40 marks

  • Quiz 1: 2.5 marks

  • Quiz 2: 2.5 marks

  • Assignment 1: 2.5 marks

  • Assignment 2: 2.5 marks

  • Course Project: 20 marks

  • Internal Examination: 10 marks

• End-semester Examination: 60 marks

Course Project

• As part of the course project, you are required to implement a machine learning algorithm using publicly available datasets. You are encouraged to select a topic from the provided list, although you are also free to explore any other area of interest. Please ensure you receive approval from the Course Instructor for your chosen topic. Additionally, you are required to submit a declaration confirming that your selected topic is distinct from the domain of your mini-project.Should the quality of your work meet the standards for academic publication, the Course Instructors may advise a joint submission for possible publication. This collaborative effort reflects the shared intellectual contribution and is a testament to the project's academic merit. 

• Suggested Project Topics:

  1. Classification of phonological categories in imagined speech Reference Paper Dataset

  2. Classification of motor imagery from EEG Reference Paper Dataset 

  3. Classification of imagined words from EEG Reference Paper Dataset

  4. Modeling wine preferences by data mining from physicochemical properties Reference Paper Dataset

  5. Breast cancer histopathological image classification using AlexNet Reference Paper Dataset

  6. Music genre classification with convolutional neural networks Reference Paper Dataset

  7. Sentiment classification system of twitter data for US airline service analysis Reference Paper Dataset

  8. Classification of emotions from EEG Reference Paper Dataset

  9. Online handwriting recognition system for Tamil Reference Paper Dataset

  10. Real-time credit card fraud detection Reference Paper Dataset

  11. Speech emotion recognition Reference Paper Dataset

  12. Boston house price prediction using regression models Reference Paper Dataset

  13. Breast cancer diagnosis Reference Paper Dataset

  14. News Classification Reference Paper Dataset

  15. Classification of sentiment polarity of cars and hotel reviews Reference Paper Dataset

  16. Classification of  fashion categories Reference Paper Dataset

  17. Prediction of Titanic survival rate Reference Paper Dataset

Quizzes 

• Quiz 1

Slides

• Module 1

• Module 2

• Module 3

• Module 4

Internals