Section outline

  • Current week

    Coding Projects

    • types of project: Coding projects can be an implementation of a method related to the course or an application of the course concepts in a new domain (e.g. NLP).
    • proposal: a preliminary proposal must be submitted for approval before starting the project. This will ensure that the project is suitable for the course. The proposal must include: the topic, a brief description of the project, and the resources that you intend to use during the project (software libraries, dataset, github repositories, papers, ...).
    • empirical validation: Implementations of ML methods must have an empirical evaluation. Unless the implemented method is very simple, a small and minimal evaluation is sufficient. Projects focusing on the application domain and using off-the-shelf methods should provide a more extensive evaluation. More specific details will be provided based on the project proposal.
    • project submission: the student must submit the project as a zip file or a link to a github repository before the exam deadline. A written report is NOT required.
    • project presentation during the oral exam: coding projects need to briefly describe the project in a short presentation (10min, max. 10 slides or a jupyter notebook), followed by questions on the results and codebase. The presentation should describe:
    1. project objectives - brief recap of the method + tooling used
    2. any critical part of the implementation - were there any implementation challenges? explain them and how you solved them
    3. overview of the results - experimental setup, model selection and hyperparameter choices, results
    4. conclusion - did you expect the results? any challenges during the empirical validation?

    Examples (more will come later):

    • implementation of an online tree ensemble (HOT, SRP, ...)
    • implementation of a meta-learning algorithm not shown in class (e.g. one of MAML, ANIL, REPTILE)
    • implementation of a class-incremental method not shown in class (e.g. IL2M, BiC)
    • implementation of a simple online CL method (DER or ER-ACE are good candidates)
    • Implementation of a federated learning method (e.g. FedSGD)

     

    Reports

    • types of project: A report is a literature review covering a small topic related to the course.
    • proposal: topic title, brief description, preliminary list of papers. The review should cover around 5 papers not covered in the lecture.
    • style: the report must be a single-column document with 10/15 pages.
    • structure: The report must describe the methodology of the chosen papers, identifying their key properties, strong/weak points of each methods, and comparing them with the literature and with the topic shown in class. Example of report structure:
    1. abstract
    2. Intro
    3. Problem Setting - using the nomenclature seen in class
    4. Methodology - explain the methods in the studied papers
    5. Analysis and Comparison between Methods - find similarities and differences between them
    6. Strengths and Weaknesses - Highlight where the methods might excel or fail, both using empirical and theoretical evidence from the papers or your intuition from the course
    7. Conclusion - draw the conclusion, recap the results of the report, and highlight any other possible question that you feel is interesting but not answered by the papers (future work)
    • project submission: the report must be submitted before the exam deadline.
    • project presentation during the oral exam: a short presentation (15min) about the report content. The suggested report structure is also good for the slides.
    Examples (more will come later):
    • a survey on concept drift detection [ref]
    • a review of online learning with linear models or online regression models (models that we have not seen in class)
    • model-based meta-learning [1]
    • meta-learned optimizers [1]
    • contrastive learning for vision
    • a review of class-incremental methods not seen in the lectures [1]
    • continual-meta-learning [1,2]
    • continual learning for embedded systems [1,2]
    • federated learning methods [1]
    • federated continual learning [1]
    • parameter-efficient training of large language models [1]

     

    Validity and Duration of the Project

    In case of a failed oral exam, the project may be kept as is or not, depending on its quality. This will be evaluated on a case by case basis. As a general rule, projects with a positive evaluation will be kept until the next session, but no later than that.