# Introduction In this lab, we will explore two probabilistic models - Probabilistic PCA and Factor Analysis - and compare their effectiveness in model selection and covariance estimation. We will perform cross-validation on low rank data that is corrupted with homoscedastic or heteroscedastic noise. In addition, we will compare the model likelihood to the likelihoods obtained from shrinkage covariance estimators. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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