Machine Learning for SPPU 20 Course (BE - SEM VII - AI&DS - 417521)

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Syllabus Machine Learning - (417521) Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Machine Learning Introduction : What is Machine Learning, Definitions and Real-life applications, Comparison of Machine learning with traditional programming, ML vs AI vs Data Science. Learning Paradigms : Learning Tasks - Descriptive and Predictive Tasks, Supervised, Unsupervised, Semi-supervised and Reinforcement Learnings. Models of Machine Learning : Geometric model, Probabilistic Models, Logical Models, Grouping and grading models, Parametric and non-parametric models. Feature Transformation : Dimensionality reduction techniques - PCA and LDA. (Chapter - 1) Unit II Regression Introduction - Regression, Need of Regression, Difference between Regression and Correlation, Types of Regression : Univariate vs. Multivariate, Linear vs. Nonlinear, Simple Linear vs. Multiple Linear, Bias-Variance tradeoff, Overfitting and Underfitting. Regression Techniques - Polynomial Regression, Stepwise Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Bayesian Linear Regression. Evaluation Metrics : Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared , Adjusted R-squared. (Chapter - 2) Unit III Classification Introduction : Need of Classification, Types of Classification (Binary and Multiclass), Binary-vs-Multiclass Classification, Balanced and Imbalanced Classification Problems. Binary Classification : Linear Classification model, Performance Evaluation - Confusion Matrix, Accuracy, Precision, Recall, F measures. Multiclass Classification : One-vs-One and One-vs-All classification techniques, Performance Evaluation - Confusion Matrix, Per Class Precision, Per Class Recall. Classification Algorithms : K Nearest Neighbor, Linear Support Vector Machines (SVM) - Introduction, Soft Margin SVM, Kernel functions - Radial Basis Kernel, Gaussian, Polynomial, Sigmoid. (Chapter - 3) Unit IV Clustering Introduction : What is clustering, Need of Clustering, Types of Clustering Hierarchical clustering algorithms / connectivity-based clustering : Agglomerative Hierarchical Clustering (AHC) algorithm, Divisive Hierarchical Clustering (DHC) algorithm. Centroid-based clustering algorithms / Partitioning clustering algorithms : K-Means clustering algorithm, Advantages and disadvantages of K-Means clustering algorithm, Elbow method, The Silhouette method, K-Medoids, K-Prototype. Density-based clustering algorithms : DBSCAN algorithm, how it works, Advantages and disadvantages of DBSCAN. Distribution-based clustering algorithms : Gaussian mixture model. Application of Clustering Technique : Market Segmentation, Statistical data analysis, Social network analysis, Image segmentation, Anomaly detection. (Chapter - 4) Unit V Ensemble Learning Ensemble Learning : Introduction to Ensemble Learning, Need of Ensemble Learning, Homogeneous and Heterogeneous ensemble methods, Advantages and Limitations of Ensemble methods, Applications of Ensemble Learning. Basic Ensemble Learning Techniques : Voting Ensemble, Types of Voting : Max Voting, Averaging, Weighted Average. Advanced Ensemble Learning Techniques : Bagging : Bootstrapping, Aggregation. Boosting : Adaptive Boosting (AdaBoost), Gradient Boosting, XGBoost. Stacking : Variance Reduction, Blending, Random Forest Ensemble, Advantages of Random Forest. (Chapter - 5) Unit VI Reinforcement Learning Reinforcement Learning : What is Reinforcement Learning ? Need for Reinforcement Learning, Supervised vs Unsupervised vs Reinforcement Learning, Types of Reinforcement, Elements of Reinforcement Learning, Real time applications of Reinforcement learning. Markov’s Decision Process : Markov property, Markov chain/process, Markov reward process (MRP), Markov decision process (MDP), Return, Policy, Value functions, Bellman equation. Q Learning : Introduction of Q-Learning, Important terms in Q learning, Q table, Q functions, Q learning algorithm. (Chapter - 6)

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Pages: 208 Edition: 2023 Vendors: Technical Publications