Syllabus Machine Learning - (BE05000211) Total Credits = TH / 30 Assessment Pattern and Marks Total Marks Theory Tutorial / Practical ESE (E) PA (M) PA (I) PBL (I) ESE (V) 03 70 30 20 30 50 200 Unit No. Content 1 Introduction What is Learning, Machine Learning, Comparison of machine learning with traditional programming, AI vs ML vs DL, Machine learning lifecycle, Types of machine learning: Supervised, Unsupervised and Reinforcement learning, Types of problems: Regression, Classification and Clustering, Dataset: Training, Testing, Underfitting, Overfitting, Bias-Variance Tradeoff. (Chapter - 1) 2 Supervised Learning - Regression Simple Linear regression, Multiple linear regression, Gradient Descent Algorithm, Polynomial Regression, Cost function. (Chapter - 2) 3 Supervised Learning – Classification Logistic Regression, Decision Tree, CART, Support Vector machine, Naïve bayes, K-NN, Dimensionality Reduction - PCA. (Chapter - 3) 4 Ensemble Learning and Model Evaluation Ensemble Methods: Bagging (Random Forest), Boosting (XGBoost), Model Evaluation and Validation: Confusion Matrix, Accuracy, Precision, Recall, ROC Curve, F1- score, Cross-validation techniques: k-fold, stratified k-fold. (Chapter - 4) 5 Unsupervised Learning Introduction of Unsupervised Learning, Application of unsupervised learning, Clustering algorithms - Partitioning Methods (k-means and k-Medoids), Hierarchical Methods (Agglomerative, Divisive), Density-based methods (DBSCAN). (Chapter - 5) 6 Advanced Topics Introduction to Neural Networks, overview of Reinforcement Learning, Components of Reinforcement learning, ML Tools: Numpy, Pandas, Tensorflow, Scikit Learn, Case Studies of Machine Learning Applications. (Chapter - 6)
