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Machine Learning Basics

  • Q. What is a closed form solution, and what are the advantages of a problem having such a solution? Which algorithms have a closed form solution?
  • Q. How does gradient descent differ from coordinate descent?
  • Q. What are the different types of Gradient Descent?
  • Q. What is Gradient Descent?
  • Q. What is the Curse of Dimensionality?
  • Q. Distinguish between Structured and Unstructured Data
  • Q. What is the difference between Supervised and Unsupervised Learning
  • Q. What are the subtypes of Cross Validation?
  • Q. How does Cross Validation Work?
  • Q. How are model hyper-parameters generally selected?
  • Q. How can overfitting be mitigated in a machine learning model?
  • Q. How can underfitting be mitigated?
  • Q. How does a learning curve give insight into whether the model is under- or over-fitting?
  • Q. What is the Bias/Variance Tradeoff?
  • Q. What is Overfitting?
  • Q. What is Underfitting?
  • Q. How does Machine Learning differ from Classical Statistics and Deep Learning?
  • Q. What is Machine Learning?
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      • Sequence models (6)
      • Transformers (9)
    • DL Basics (16)
    • DL Training and Optimization (17)
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  • Supervised Learning (115)
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      • Linear Regression (26)
      • Regularization (6)
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      • Distance Measures (9)
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      • Hierarchical Clustering (3)
      • K-Means Clustering (9)
    • Dimensionality Reduction (9)
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Other Questions in Machine Learning Basics
  • What is Within Cluster Sum of Squares (WCSS)?
  • What are some approaches for modeling non linear relationships?
  • What is Kernel PCA?
  • What problems would arise from using a regular linear regression to model a binary outcome?
  • What is the problem with using a generic list of stop words? 
  • What is Classification?
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