Module #11 Principal Component Analysis (PCA) Introduction to PCA, dimensionality reduction, and feature extraction
Module #12 Reinforcement Learning Introduction to reinforcement learning, Markov decision processes, and Q-learning
Module #13 Neural Networks Introduction to neural networks, perceptron, and multi-layer perceptron
Module #14 Deep Learning Introduction to deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
Module #15 Natural Language Processing (NLP) Introduction to NLP, text preprocessing, and text classification
Module #16 Machine Learning in Software Engineering Applications of machine learning in software engineering, defect prediction, and effort estimation
Module #17 Software Quality Prediction Predicting software quality metrics, such as bugs, faults, and failures
Module #18 Requirement Engineering with Machine Learning Applications of machine learning in requirement engineering, requirement prioritization, and requirement classification
Module #19 Machine Learning for Testing Applications of machine learning in software testing, test case generation, and test data generation
Module #20 Machine Learning for Maintenance Applications of machine learning in software maintenance, bug localization, and code smell detection
Module #21 Explainability and Interpretability of Machine Learning Models Techniques for explaining and interpreting machine learning models, LIME, and SHAP
Module #22 Machine Learning Ethics Ethical considerations in machine learning, bias, and fairness
Module #23 Machine Learning Model Deployment Deploying machine learning models, model serving, and model management
Module #24 Machine Learning in DevOps Applications of machine learning in DevOps, continuous integration, and continuous deployment
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning in Software Engineering career
