Machine Learning Lab PCCSL508 Semester 5 KTU CS 2024 Scheme manual - Dr Binu V P

About Me- Dr Binu V P

Syllabus Machine Learning Lab PCCSL508 KTU 2024 Scheme

Learn Python well before You start(focus on numpy,pandas,matplotlib )- refer blog

Build a strong understanding of the theory before moving on to programming

Recommended Tools and Setup for Lab

Lab Assignments

• Lab Assignment-1
• Lab Assignment-2
• Lab Assignment-3
• Lab Assignment-4
• Lab Assignment-5
• Lab Assignment-6
• Lab Assignment-7
• Lab Assignment-8
• Lab Assignment-9
• Lab Assignment-10

Experiments

Regression 

• Explore Californoa Housing Dataset(learn pandas,scikit-learn and matplotlib)
• Simple Linear Regression using Sample Data ( Single variable, Toy Example)
• Simpe Linear Regression using Sample Data ( using scikit-learn)
• Simple Linear regression using California Housing Dataset(CSV input)
• Simple Linear Regression Using California Housing Dataset (using scikit-learn)

• Simple Linear Regression using Gradient Descent ( Single variable, Toy Example)
• Simple Linear regression using Gradient Descent on California Housing Dataset

• Multiple Linear Regression using using Matrix Form ( Toy Dataset)
• Multiple Linear Regression using scikit-learn for House Price Prediction(synthetic data)

• Multivariate Linear Regression using Gradient Descent (Toy Dataset)
• Implement linear regression using scikit-learn on  California Housing dataset( Multi Variable)

Polynomial Regression

• Polynomial Regression with varying degrees ( toy example)
• Polynomial Regression using Grid Search for Optimal Degree Selection
• Explore Auto MPG Data set 
• Implement polynomial regression on the Auto MPG dataset with comparison to Linear Regression
• Bias–Variance Tradeoff using Polynomial Regression on Housing Dataset

L1 and L2 Regularization

• Comparison of Linear, Ridge, and Lasso Regression with Model Evaluation
• Implement Ridge and Lasso regression on the Diabetes dataset

Logistic Regression

• Logistic Regression  using gradient descent (sample dataset)
• Logistic Regression with Evaluation Metrics and Decision Boundary Visualization( sample data)
• Logistic Regression on Pima Indians Diabetes Dataset- (Download the pima indians diabetes dataset)
• Logistic Regression with assessments Confusion Matrix, ROC and AUC

MLE and MAP

• Comparison of MLE and MAP Estimation using Sample Data
• Comparison of MLE and MAP Estimation - Beta Prior
• Comparison of MLE and MAP Estimation- Gaussian Prior
• Parameter Estimation in Logistic Regression using MLE and MAP (sample data)
• Logistic Regression using MLE and MAP (L1 & L2 Regularization) on Breast Cancer Dataset
• Multinomial Parameter Estimation using MLE and MAP (Dirichlet Prior) on 20Newsgroups dataset

Naive Bayes Classifier

• Prediction of Playing Golf using Naive Bayes Classifier ( Toy example)
• Implementation of Bernoulli Naive Bayes Classifier Using Scikit-Learn
• Implementation of Categorical Naive Bayes Classifier using Scikit-Learn
• Comparison of Multinomial and Bernoulli Naive Bayes on Sample Data
• Text Classification using Multinomial vs Bernoulli Naïve Bayes (Download 20 Newsgroups Dataset)

KNN

• Implementation of K-Nearest Neighbors (KNN) Classification Algorithm-binary class ( toy example)
• Finding the Optimal Value of K in KNN using Grid Search
• KNN for Multi-Class Classification with Visualization and Distance-Weighted Voting
• KNN Classification using Real Dataset with Performance Evaluation (Download Breast Cancer Data set)
• Image Classification using K-Nearest Neighbors (KNN) on Fashion MNIST Dataset

Decision Tree

• Basics of Decision Tree using ID3 Algorithm (Iterative Dichotomiser3 Algorithm )
• Implementation of Decision Tree using ID3 Algorithm with Visualization (scikit-learn)
• Customer Segmentation using Decision Tree (ID3) on Online Retail Dataset ( Download dataset).
• Comparison of Logistic Regression and Decision Tree on Adult Income Dataset (Download dataset ).
  
  

SVM

• Implementation of Linear SVM from Scratch using sample data
• Implementation of linear SVM using Scikit-learn with Visualization (sample data)
• Classification of Non-Linear Data using SVM with RBF Kernel ( sample data)
• Linear SVM Classification on Iris Dataset with Decision Boundary Visualization ( Download iris data set )
• Performance Comparison of SVM Kernels on Fashion MNIST Dataset

Neural Network

• Neural Network for OR Classification with Visualization ( single layer NN)
• Multi Layer Neural Network for XOR Classification with Visualization
• Multi Layer Neural Network for XOR Classification (gradient descent from scratch)
• Implementation of XOR Problem using Neural Network in scikit-learn
• Implementation of XOR Problem using Neural Network in TensorFlow/Keras
• Implementation of XOR Problem using a Small Neural Network in TensorFlow/Keras and Visualization
• Implementation of Multilayer Feed-Forward Neural Network (MLP) on Wine Quality Dataset using Keras ( Download Wine Quality Dataset)
• Implementation of Multilayer Feed-Forward Neural Network (MLP) on Wine Quality Dataset with Architecture Comparison
• Comparison of Activation Functions (Sigmoid, ReLU, Tanh) using Neural Network on MNIST Dataset
• Hyperparameter Tuning of Neural Network on Fashion MNIST Dataset

Clustering

• Implementation of K-Means Clustering ( toy example-manual method)
• Implementation of K-Means Clustering Using Scikit-Learn
• Implementation of K-Means Clustering on Randomly Generated Data with 5 Clusters
• Determining Optimal K using Elbow Method
• Determining Optimal K using Silhouette Method
• Determining Optimal K using Grid Search and Applying K-Means
• K-Means Clustering on Digits Dataset with Performance Evaluation
• Agglomerative Clustering and Dendrogram Visualization (Without Library)
• Agglomerative Clustering and Dendrogram Visualization using Scikit-Learn
• Comparison of K-Means and Agglomerative Clustering on Mall Customers Dataset (Download Mall Customers Dataset)

Resampling Methods

• Classification using Iris Dataset with K-Fold Cross Validation
• Bootstrapping with Iris Dataset
• Bootstrapping vs K-Fold Cross Validation (Iris Dataset)

Ensemble  Methods

• Random Forest Regression and Visualization ( Sample Data - Undertsand the concepts)
• Random Forest Regression vs Linear Regression
• Implementation of Bagging using Random Forest  on Titanic Dataset.(Download  Titanic Dataset)
• Implementation of Boosting using AdaBoost on Titanic Dataset
• Manual Implementation of Gradient Boosting Regression using Sample Data
• Implementation of Boosting using Gradient Boosting on Titanic Dataset
• Implementation of Boosting using XGBoost on Titanic Dataset
• Implementation and Comparison of Bagging and Boosting on Titanic Dataset