Machine Learning for SPPU 19 Course (BE - SEM VII -COMP) - 410242

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Unit I Introduction To Machine Learning Introduction to Machine Learning, Comparison of Machine learning with traditional programming, ML vs AI vs Data Science. Types of learning : Supervised, Unsupervised and semi-supervised, reinforcement learning techniques, Models of Machine learning : Geometric model, Probabilistic Models, Logical Models, Grouping and grading models, Parametric and non-parametric models. Important Elements of Machine Learning - Data formats, Learnability, Statistical learning approaches. (Chapter - 1) Unit II Feature Engineering Concept of Feature, Preprocessing of data : Normalization and Scaling, Standardization, Managing missing values, Introduction to Dimensionality Reduction, Principal Component Analysis (PCA), Feature Extraction : Kernel PCA, Local Binary Pattern. Introduction to various Feature Selection Techniques, Sequential Forward Selection, Sequential Backward Selection. Statistical feature engineering : count-based, Length, Mean, Median, Mode etc. based feature vectorcreation. Multidimensional Scaling, Matrix Factorization Techniques. (Chapter - 2) 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)

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Pages: 204 Edition: 2023 Vendors: Technical Publications