| CO1 | Apply the concepts of supervised and unsupervised machine learning algorithms |
| CO2 | Apply artificial neural networks and fuzzy inference systems to solve problems involving uncertainty and pattern recognition |
| CO3 | Analyze search algorithms and reinforcement learning techniques for intelligent decision-making |
| CO4 | Demonstrate the application of machine learning and AI in Smart grids and electric vehicles |
Course Articulation Matrix: Correlation level [ 1: low, 2: medium, 3: High]
| PO | PO1 | PO2 | PO3 | PSO1 | PSO2 |
| CO | |||||
| CO1 | 1 | 1 | 2 | – | – |
| CO2 | 2 | 1 | 2 | – | – |
| CO3 | 2 | 1 | 3 | – | – |
| CO4 | 3 | 1 | 3 | 3 | 1 |
Introduction to statistics and Data preprocessing. Dimensionality reduction techniques, Machine Learning for classification and regression: Supervised Learning, Linear Regression, Logistic Regression, Support Vector Machine, Tree Models, Naïve Bayes, Ensemble Learning; Unsupervised Learning-Clustering
Introduction to Neural Networks, MLPs, Deep Neural Networks: architecture and transfer learning, Recurrent Neural Networks. Fuzzy Inference System, Neuro-Fuzzy.
Search algorithm: Depth first search, Breadth first search, A-star Algorithm.
Reinforcement Learning: Classical Reinforcement Learning methods.
Hyper-paramerter tuning, Model Evaluation Metrics.
Case study/Coding Lab for Applications in SmartGrid and Electric Vehicles