CO-PO Mapping

Evaluation Pattern

• Continuous Internal Evaluation: 40 marks

  • Attendance: 5 marks

  • Quiz: 10 marks

  • Course Project: 5 marks

  • Internal Examination 1: 10 marks

  • Internal Examination 1: 10 marks

• End-semester Examination: 60 marks

Course Project

As part of this course, you are required to implement a machine learning algorithm using a publicly available dataset. You are encouraged to choose a project topic from the suggested list provided below, or propose your own topic for approval.

Project Guidelines

• Projects can be done individually or in groups of up to 5 students.
  
  

• You must get your project topic approved by the course instructor before the 5th lecture.
  
  

• The suggested topics are deliberately challenging, intended to push your limits and deepen your understanding of AI and data science.
  
  

• The final result is not what matters most—the knowledge, skills, and insights you gain during the process are far more important.
  
  

Key Focus Areas

• Apply the concepts learned in class to a real-world or research-grade dataset.
  
  

• Experiment, fail, learn, and iterate—don’t focus only on achieving perfect accuracy.
  
  

• Document your methodology, challenges, and learnings clearly

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. Student Performance Prediction Reference Paper Dataset

  13. Heart Disease Prediction Reference Paper Dataset

  14. Loan Approval Prediction  Reference Paper Dataset

  15. House Price Prediction  Reference Paper Dataset

  16. MNIST Digit Recognition Reference Paper Dataset

  17. Fake News Prediction Reference Paper Dataset

  18. Crop Recommendation Using Soil & Weather Data Reference Paper Dataset

Course Coverage and Attendance

Available in ETLAB

Internals

Available in ETLAB

Lecture Slides

• Slides (Alternate Link)

Colab Notebooks

• Lecture 2: There are two L modes, one for predicting age and another for classifying gender. The aim is to tell the students that numberse are everything for an ML model. 

Rubrics for Assessment of Project

Tutorial

Quiz