Machine Learning

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B.Tech Course Syllabus Even Sem 2025-26

• B.Tech IT Sem 6 Syllabus: Machine Learning [ Syllabus PDF ]

Course Materials (PPTs/PDFs/Handouts):

• 1. Introduction to Machine Learning

     • Introduction to Machine Learning [ PDF ]

     • Training set vs Testing set vs Validation set [ Goolge Colab ]

     • Trade-off between Bias and Variance [ PDF ]

     • Steps for developing a ML Application [ Google Colab ]

     • Evaluation Metrics and Learning Algorithms PDF

       • Confusion Matrix  - Jan 2026 [ PDF ] [ Spreadsheet ]

  2. Dimensionality Reduction

     • Eigenvalues and Eigenvectors [ PDF ]

     • Covariance Matrix

       • Covariance Matrix Example 1 PDF | Example 2 PDF

     • Principal Component Analysis (PCA) [ PDF ]

       • Solved Example 1 PDF | Example 2 PDF | Complete Example PDF

     • Independent Component Analysis (ICA) PDF

       • ICA Solved Example [ PDF ]

     • Singular Value Decompostion (SVD) [ PDF ]

       • SVD Solved Example 1 PDF | Example 2 PDF

       • SVD Solved Example 4 PDF | Example 5 PDF

     • Linear Discriminant Analysis (LDA) [ PDF ]

       • LDA Example 1 PDF

     • Applications of Dimensionality Reduction

       • Image Compression [ PDF ]

       • Feature Extraction for Information Retrieval [ PDF ]

  3. Classification

     • K-Nearest Neighbor PDF

       • KNN Solved Example 1 PDF | Example 2 PDF | Example 3 PDF

       • Regression using KNN Example 1 PDF

     • Logistic Regression PDF

     • Support Vector Machine PDF

       • Linear SVM Solved Example PDF

       • Non-Linear SVM Solved Example PDF

     • Random Forest RF [ PDF ]

       • Decision Tree - Classification [ PDF ]

       • Decision Tree - Regression [ PDF ]

       • Classification and Regression Trees (CART) PDF

       • Random Forest RF Solved Example 1 [ PDF ]

     • Applications of Classification

       • Image Recognition [ PDF ]

       • Fraud Detection [ PDF ]

  4. Regression

     • Logistic Regression PDF

       • Role of Log Odds in Logistic Regression PDF

       • Logistic Regression Solved Example 1 [ PDF ]

       • Logistic Regression Solved Example 2 [ PDF ]

       • Logistic Regression over dataset PDF

     • Simple Linear Regression PDF

       • Solved Example 1 PDF | Example 2 PDF | Example 3 PDF

       • Method 2: SLR Solved Example 4 PDF

     • Multiple Linear Regression PDF

       • MLR Solved Example 1 PDF | Example 2 PDF

     • Regularized Linear Regression PDF

       • Ridge Regression | Lasso Regression | Elastic Net Regression

     • Support Vector Regression (SVR) i.e SVM Regression PDF

     • Classification and Regression Trees (CART) PDF

       • CART Solved Example [ PDF ]

       • Extra Reading for understanding CART

         • Decision Trees - Classification PDF

         • Decision Trees - Regression PDF

     • Applications of Regression

       • Weather Prediction

       • Stock Market Predication

  5. Clustering

     • k-Mean Clustering [ PDF ]

       • Solved Example 1 [ PDF ]

       • Solved Example 1 [ PDF ]

     • Density Based Clustering: DBSCAN [ PDF ]

     • Fuzzy Clustering [ PDF ]

       • FCM Solved Example 1 [ PDF ]

       • FCM Solved Example 2 [ PNG ]

     • Expectation Maximization (EM) [ PDF ]

     • Applications of Clustering

       • Sentiment Analysis  [ PDF ]

       • Recommender Systems [ PDF ]

  6. Reinforcement Learning

     • Introduction to Reinforcement Learning [ PDF ]

     • Formalism in Reinforcement Learning (RL) 

       • Markov Decision Process (MDP) [ PDF ]

     • Reinforcement Learning Algorithms

       • Q-Learning [ PDF ]

       • State-Action-Reward-State-Action (SARSA) [ PDF ]

     • Applications of Reinforcement Learning Techniques

       • AlphaGo Zero [ PDF ]

         • https://doi.org/10.1051/itmconf/20257801016 

         • https://timallanwheeler.com/data/writeups/2017_alphago_zero.pdf

       • Robotics and Industrial Automation [ PDF ]

💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 💻  💻💻 💻 💻 💻 💻 💻 💻 

Lab Experiments

• Guidelines for writing Lab Experiments - Jan 2023 [ PDF ]

• List of Datasets:  

  • insurance.csv 

  • heart.csv 

  • iris.csv 

  • kerala.csv

  • diabetes.csv

1. Introduction to Machine Learning

   • EDA on Insurance Dataset  [  ipython ] [ insurance.csv ]

   • Training Dataset vs Testing Dataset vs Validation Dataset [ ipython ]

   • Implement Machine Learning Steps in Python  [  ipython ]

   • Data Visualization Graph and Plots  [  ipython ]

   • Basics of Python   [  ipython ]

2. Dimensionality Reduction

   • Dimensionality Reduction using PCA [ ipython ]

   • Dimensionality Reduction using SVD [ ipython ]

   • Implementation of Singular Value Decomposition (SVD) [ ipython ]

3. Classification

   • To implement Support Vector Machine

     • Heart Disease Prediction using SVM  [  ipython ]

     • Breast Cancer Prediction using SVM Classifier [ ipython ]

   • To implement Bayesian Classification

   • To implement Hidden Hidden Markov Model

     • Weather Prediction using HMM [ ipython ]

     • Speech recognition using HMM  [ ipython ]

   • To Implement Random Forest

     • Heart Disease Prediction using Random Forest  [  ipython ]

   • To implement K-Nearest Neighbour

     • Heart Disease Prediction k-NN Classifier  [  ipython ]

4. Regression

   • To implement Linear Regression

   • To implement Logistic Regression 

     • Heart Disease Prediction using Logistic Regression  [  ipython ]

   • Decision Tree Learning

     • Decision Tree Learning using iris Dataset   [  ipython ]

     • Kerala Flood Prediction using Decision Tree   [  ipython ]

   • To implement CART decision tree algorithm

5. Clustering

   • Density-Based Clustering: DBSCAN [ ipython ]

   • To implement Radial basis functions

6. Reinforcement Learning

   • Reinforcement Learning Algorithms

     • Q-Learning [ PDF ]

   • State-Action-Reward-State-Action (SARSA) [ PDF ]

   • Mini project based on any machine learning application

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Lab Manual

• Sample Gray Scale 8-bit BMP Images for experiment [ IMAGE FOLDER ]

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• Machine Learning using Python = Lab Manual - July 2020 [ PDF ]

B.Tech Course Syllabus Even Sem 2024-25

• B.Tech IT Sem 6 Syllabus: Machine Learning Syllabus PDF

Course Materials (PPTs/PDFs/Handouts):

• 1. Introduction

     • Introduction to Machine Learning PDF

     • Evaluation of Learning Algorithms PDF

       • Model Evaluation (Regularization) - 25 Jan 2025 PDF

       • Model Improvement (Ensemble Learning) - 25 Jan 2025 PDF

       • Model Optimization - 25 Jan 2025 PDF

       • Confusion Matrix  - Jan 2025 PDF  Spreadsheet

     • Inferential Statistics: Hypotheses Testing LaTeX PDF

     • Steps for developing a ML Application Google Colab

     • Training Dataset vs Testing Dataset vs Validation Dataset Goolge Colab

     • Trade-off between Bias and Variance [ PDF ]

  2. Learning with Regression and Trees

     • Learning with Regression

       • Simple Linear Regression PDF

         • Linear Regression Example 1 PDF

         • Linear Regression Example 2 PDF

       • Multiple Linear Regression PDF

         • Multiple Regration Solved Example PDF

       • Logistic Regression PDF

         • Role of Log Odds in Logistic Regression PDF

         • Logistic Regression Solved Example 1 [ PDF ]

         • Logistic Regression Solved Example 2 [ PDF ]

     • Learning with Trees

       • Decision Trees - Classification PDF

       • Decision Trees - Regression PDF

       • Classification and Regression Trees (CART) PDF

         • CART Solved Example [ PDF ]

  3. Dimensionality Reduction

     • Principal Component Analysis (PCA) PDF

       • PCA Solved Example 1 PDF

       • PCA Solved Example 2 PDF

     • Independent Component Analysis (ICA) PDF

       • ICA Solved Example [ PDF ]

     • Singular Value Decompostion (SVD) PDF | 

       • SVD Solved Example 1 PDF

       • SVD Solved Example 2 PDF

       • SVD Solved Example 4 PDF

       • SVD Solved Example 5 PDF

  4. Classification

     • Bayesian Belief Networks (BBN) PDF

       • BBN Solved Example PDF

     • Hidden Markov Models (HMM) PDF

       • HMM Solved Example [ PDF ]

     • Support Vector Machine PDF

       • SVM Solved Example PDF

     • K-Nearest Neighbor PDF

       • KNN Solved Example PDF

  5. Clustering

     • Introduction Clustering

     • Density Based Clustering: DBSCAN [ PDF ]

     • Expectation Maximization (EM) [ PDF ]

     • Radial Basis functions [ PDF ]

       • RBF Solved Example [ PDF ]

  6. Applications of Machine Learning

     • Recommender Systems [ PDF ]

     • Image Recognition [ PDF ]

     • Sentiment Analysis  [ PDF ]

     • Machine Learning for Video Surveillance  [ PDF ]

💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 💻  💻 

Lab Experiments

• Guidelines for writing Lab Experiments - Jan 2023 [ PDF ]

• List of Datasets: 

  • insurance.csv

  • heart.csv

  • iris.csv

  • kerala.csv

1. Introduction to Machine Learning

   • EDA on Insurance Dataset  [  ipython ] [ insurance.csv ]

   • Training Dataset vs Testing Dataset vs Validation Dataset [ ipython ]

   • Implement Machine Learning Steps in Python  [  ipython ]

   • Data Visualization Graph and Plots  [  ipython ]

   • Basics of Python   [  ipython ]

2. Learning with Regression and Trees

   • To implement Linear Regression

   • To implement Logistic Regression 

     • Heart Disease Prediction using Logistic Regression  [  ipython ]

   • Decision Tree Learning

     • Decision Tree Learning using iris Dataset   [  ipython ]

     • Kerala Flood Prediction using Decision Tree   [  ipython ]

   • To implement CART decision tree algorithm

3. Dimensionality Reduction

   • Dimensionality Reduction using PCA [ ipython ]

   • Dimensionality Reduction using SVD [ ipython ]

   • Implementation of Singular Value Decomposition (SVD) [ ipython ]

4. Classification

   • To implement Support Vector Machine

     • Heart Disease Prediction using SVM  [  ipython ]

     • Breast Cancer Prediction using SVM Classifier [ ipython ]

   • To implement Bayesian Classification

   • To implement Hidden Hidden Markov Model

     • Weather Prediction using HMM [ ipython ]

     • Speech recognition using HMM  [ ipython ]

   • To Implement Random Forest

     • Heart Disease Prediction using Random Forest  [  ipython ]

   • To implement K-Nearest Neighbour

     • Heart Disease Prediction k-NN Classifier  [  ipython ]

5. Clustering

   • Density-Based Clustering: DBSCAN [ ipython ]

   • To implement Radial basis functions

6. Applications of Machine Learning

   • Mini project based on any machine learning application

🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁🏁

Lab Manual

• Sample Gray Scale 8-bit BMP Images for experiment [ IMAGE FOLDER ]

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• Machine Learning using Python = Lab Manual - July 2020 [ PDF ]