The official GTU syllabus could not be verified here, so chapters are derived from the questions, aligned to the usual GTU unit flow (introduction/learning -> features/dimensionality reduction -> supervised models -> Bayesian -> SVM -> neural networks -> ensembles -> computational learning -> clustering). Unit numbers are approximate.

Code	Chapter	Scope
C1	Ch1: Introduction, Learning Types & Model Evaluation (Unit 1)	ML definition/applications, learning types, hypothesis, overfitting, bias-variance, cross-validation, metrics, supervised learning
C2	Ch2: Features, Feature Selection & Dimensionality Reduction	feature selection/extraction, dimensionality reduction, PCA, preprocessing, features
C3	Ch3: Regression, Decision Tree & kNN	linear/logistic regression, decision tree, kNN, collaborative filtering
C4	Ch4: Bayesian Learning & Naive Bayes	Bayes theorem, prior/posterior, Naive Bayes, Bayesian numericals
C5	Ch5: Support Vector Machine	SVM, margin, kernels, soft margin, applications
C6	Ch6: Neural Networks & Deep Learning	neuron, perceptron, activation functions, FFNN, backpropagation, RNN, ADALINE, deep learning
C7	Ch7: Ensemble Learning	bagging, boosting, AdaBoost, random forest, voting/stacking
C8	Ch8: Computational Learning Theory (VC Dimension)	VC dimension, sample bounds, computational learning models
C9	Ch9: Clustering	k-means, hierarchical, clustering vs classification, criteria