KTU 2024 Scheme · Semester 5 · Common to CS/CA
Machine Learning Lab (PCCSL508) Syllabus
Official KTU 2024 Scheme syllabus for Machine Learning Lab, Semester 5, Common to CS/CA (Computer Science and Engineering).
Course Code
PCCSL508
Credits
2
Teaching Hours
0:0:3:0 (L:T:P:R)
CIE Marks
50
ESE Marks
50
Exam Duration
2 Hrs 30 Min
Prerequisites
None
Semester
Semester 5
Course Objective
To give the learner a practical experience of the various machine learning techniques and be able to demonstrate them using a language of choice.
Module-wise Syllabus
Module 1
Linear and polynomial regression on real datasets (California Housing, Auto MPG) with gradient descent and normal-equation solutions; Ridge/Lasso regression on the Diabetes dataset; logistic regression with MLE/MAP estimation on the Breast Cancer Wisconsin dataset; MLE/MAP for multinomial distribution parameters on 20 Newsgroups; logistic regression with/without feature scaling on Pima Indians Diabetes; Multinomial vs Bernoulli Naive Bayes text classification on 20 Newsgroups; KNN image classification on Fashion MNIST; ID3 decision tree on Online Retail customer segmentation; Logistic Regression vs Decision Trees on Adult Income; Linear SVM on Iris with decision-boundary visualization; SVM kernel comparison (linear, polynomial, RBF) on Fashion MNIST; MLP architecture experiments on Wine Quality; activation function comparison (Sigmoid, ReLU, Tanh) on MNIST; hyperparameter tuning on Fashion MNIST; hierarchical vs K-means clustering on Mall Customers; K-means cluster-count experiments on the Digits dataset; bootstrapping vs cross-validation on Iris; bagging vs boosting ensembles on Titanic; bias-variance tradeoff via polynomial regression on Boston Housing.
Course Outcomes
- CO1Understand the complexity of machine learning algorithms and their limitations.
- CO2Understand modern notions in data analysis-oriented computing.
- CO3Apply common machine learning algorithms in practice and implement their own.
- CO4Perform experiments in machine learning using real-world data.
Assessment Pattern (CIE: 50 marks, ESE: 50 marks)
Continuous Internal Evaluation (CIE)
| Attendance | 5 |
| Preparation / Pre-Lab Work, Viva, Timely Record Completion (Continuous Assessment) | 25 |
| Internal Examination | 20 |
End Semester Examination (ESE)
Total 50 marks, 2 Hrs 30 Min. See the official KTU syllabus document for the exact Part A / Part B question pattern for this course.
Textbooks & Reference Books
Textbooks
- Introduction to Machine Learning — Ethem Alpaydin (MIT Press, 4th edition, 2020)
- Machine Learning using Python — Manaranjan Pradhan, U Dinesh Kumar (Wiley, 1st edition, 2019)
- Machine Learning: Theory and Practice — M.N. Murty, V.S. Ananthanarayana (Universities Press, 1st edition, 2024)
Reference Books
- Data Mining and Analysis: Fundamental Concepts and Algorithms — Mohammed J. Zaki, Wagner Meira (Cambridge University Press, 1st edition, 2016)
- Neural Networks for Pattern Recognition — Christopher Bishop (Oxford University Press, 1st edition, 1998)
Frequently Asked Questions
How many credits is KTU Machine Learning Lab (PCCSL508)?
2 credits, with 0:0:3:0 (L:T:P:R) teaching hours per week, under the KTU 2024 Scheme.
How many modules are in the PCCSL508 syllabus?
1 modules.
What is the CIE and ESE mark split for this course?
CIE (Continuous Internal Evaluation): 50 marks. ESE (End Semester Examination): 50 marks, 2 Hrs 30 Min. Total: 100 marks.
What are the recommended textbooks for PCCSL508?
Introduction to Machine Learning (Ethem Alpaydin); Machine Learning using Python (Manaranjan Pradhan, U Dinesh Kumar); Machine Learning: Theory and Practice (M.N. Murty, V.S. Ananthanarayana).
Is this syllabus specific to one branch, or common to others too?
This Semester 5 course is listed under Common to CS/CA at KTU under the 2024 Scheme — check the course header above for which branches it's common to.
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