What to expect in a university grad ML software engineer interview? Also some best ML related resources to study for the interview?
Decision trees, logistic regression, k means, KNN, cosine similarity
td lambda, q learning, ceq, LP
Can you expand on those acronyms please?
temporal difference, q is just q, correlated equillibrium Q and linear programming
Basic questions about all aspects of ml, such as overfit, underfit, PR AUC, logloss, model complexity, tree learners, svm, lr, neural nets. Check Andrew ng’s course on coursera. If you want a deep dive also take his deep learning specification course.