Syllabus Machine Learning - (410242) Credit Examination Scheme : 03 End-Sem (Paper) : 70 Marks Unit III Supervised Learning : Regression Bias, Variance, Generalization, Underfitting, Overfitting, Linear regression, Regression : Lasso regression, Ridge regression, Gradient descent algorithm. Evaluation Metrics : MAE, RMSE, R2 (Chapter - 3) Unit IV Supervised Learning : Classification Classification : K-nearest neighbour, Support vector machine. Ensemble Learning : Bagging, Boosting, Random Forest, Adaboost. Binary-vs-Multiclass Classification, Balanced and Imbalanced Multiclass Classification Problems, Variants of Multiclass Classification: One-vs-One and One-vs-All Evaluation Metrics and Score : Accuracy, Precision, Recall, Fscore, Cross-validation, Micro-Average Precision and Recall, Micro-Average F-score, Macro-Average Precision and Recall, Macro-Average F-score. (Chapter - 4) Unit V Unsupervised Learning K-Means, K-medoids, Hierarchical, and Density-based Clustering, Spectral Clustering. Outlier analysis: introduction of isolation factor, local outlier factor. Evaluation metrics and score : elbow method, extrinsic and intrinsic methods (Chapter - 5) Unit VI Introduction To Neural Networks Artificial Neural Networks : Single Layer Neural Network, Multilayer Perceptron, Back Propagation Learning, Functional Link Artificial Neural Network, and Radial Basis Function Network, Activation functions, Introduction to Recurrent Neural Networks and Convolutional Neural Networks (Chapter - 6)
