Explain ML basics and recommender tuning

Give a clear explanation of each machine learning topic below and talk through the practical trade-offs involved:

• overfitting and common approaches for avoiding it,
• bagging and the situations where it is useful,
• linear regression,
• logistic regression,
• transformer models,
• SGD versus Adam. After that, describe how you would tune model hyperparameters in an actual production environment. Last, discuss a recommendation system you have worked on or would build in practice: how you would set up the problem, choose features and models, train and evaluate the system, tune it, and address real-world concerns such as cold start, feedback loops, and online experimentation. Overview: This question tests core machine learning knowledge, including overfitting and regularization, ensemble methods such as bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), production hyperparameter tuning, and end-to-end recommender system design and deployment.