Unit 1Introduction to AI & ML History of AI, Comparison of AI with Data Science, Need of AI in Mechanical Engineering, Introduction to Machine Learning. Basics : Reasoning, problem solving, Knowledge representation, Planning, Learning, Perception, Motion and manipulation. Approaches to AI : Cybernetics and brain simulation, Symbolic, Sub-symbolic, Statistical. Approaches to ML : Supervised learning, Unsupervised learning, Reinforcement learning. (Chapter - 1) Unit 2Feature Extraction and Selection Feature extraction : Statistical features, Principal Component Analysis. Feature selection : Ranking, Decision tree - Entropy reduction and information gain, Exhaustive, best first, Greedy forward & backward, Applications of feature extraction and selection algorithms in Mechanical Engineering. (Chapter - 2) Unit 3Classification & 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 4Development 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 5Reinforced 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)
