# Introduction Gaussian processes are a popular tool in machine learning for regression and classification tasks. However, they typically require data to be in fixed-length feature vector form, which can be limiting in certain applications. In this lab, we will explore how Gaussian processes can be used on variable-length sequences, such as gene sequences, by defining a kernel function that operates directly on these structures. We will use scikit-learn to implement our Gaussian process models. ## 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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