Syllabus Artificial Intelligence & Machine Learning (302049) Credits Examination Scheme Theory 3 End-Semester 70 Marks Practical 1 Oral 25 Marks Unit 3 Classification & Regression Classification : Decision tree, Random forest, Naive Bayes, Support vector machine. Regression : Logistic Regression, Support Vector Regression. Regression trees : Decision tree, random forest, K-Means, K-Nearest Neighbor (KNN). Applications of classification and regression algorithms in Mechanical Engineering. (Chapter - 3) Unit 4 Development of ML Model Problem identification : classification, clustering, regression, ranking. Steps in ML modeling, Data Collection, Data pre-processing, Model Selection, Model training (Training, Testing, K-fold Cross Validation), Model evaluation (understanding and interpretation of confusion matrix, Accuracy, Precision, Recall, True positive, false positive etc.), Hyper parameter Tuning, Predictions. (Chapter - 4) Unit 5 Reinforced and Deep Learning Characteristics of reinforced learning; Algorithms : Value Based, Policy Based, Model Based; Positive vs Negative Reinforced Learning; Models: Markov Decision Process, Q Learning. Characteristics of Deep Learning, Artificial Neural Network, Convolution Neural Network. Application of Reinforced and Deep Learning in Mechanical Engineering. (Chapter - 5) Unit 6 Applications Human Machine Interaction, Predictive Maintenance and Health Management, Fault Detection, Dynamic System Order Reduction, Image based part classification, Process Optimization, Material Inspection, Tuning of control algorithms. (Chapter - 6)
