• Attention, Transformers, LLMs
    [Topics] [Practical]
    Advanced Convolutional Neural Network Architectures (2012 - 2018)
    [Topics] [Practical]
    Network Pruning
    [Topics] [Practical]
    Deep Autoencoders and Variational Autoencoders
    [Topics] [Practical]
    Noise Reduction in Machine Learning
    [Topics] [Practical]


  • Attention is all you need

      [ Notebook (DIY) ] [ Notebook (Soln.) ]

    Paper: Vaswani, Ashish, et al. "Attention is all you need." NeurIPS 2017.
    Task: Neural Machine Translation (e.g, German-English)
    Dataset: Multi30k
    Libraries: PyTorch, NLTK, Spacy, torchtext

    Learning objectives:
    • Build a transformer model for neural machine translation
    • Train the model using proposed label smoothing loss and learning rate scheduler
    • Use the trained model to infer likely translations using
      • Greedy Decoding
      • Beam Search
    Molecule Attention Transformer

      [ Notebook (Soln.) ]

    Paper: Maziarka, et al. "Molecule Attention Transformer"
    Task: Classification task to predict Blood-brain barrier permeability (BBBP)
    Dataset: BBBP
    Libraries: PyTorch, DeepChem, RDKit

    Learning objectives:
    • Learn key concepts required to work with molecules
    • Perform critical data preprocessing tasks, such as feature extraction, graph formation, and scaffold splitting
    • Explore challenges of drug discovery, particularly designing drugs that can cross the blood-brain barrier and enter the central nervous system
    • Implement Molecule Attention Transformer (MAT) proposed by Maziarka et al. (2020) using a deep learning pipeline
    • Train and evaluate the model on molecular datasets
    Accessing Research Data for Social Science [Oxford Internet Institute, MT 2022]

      [ Notebook (DIY) ]

    DeepNote: (Jupyter notebook hosting service) DIY Notebooks.
    Github: Repository to work on your local machine.
    Programming Language: Python
    Libraries: Pandas, feedparser, newscatcherapi, psaw, requests, twarc (Twitter API), requests-html
    Learning objectives:
    • Use Python to collect research data from the social web
    • Give due consideration to the ethics of data collection
    • Following topics are covered:
      • Accessing RSS feeds
      • Accessing Reddit and Wikipedia through APIs
      • Accessing Twitter using Twitter API
      • Web crawling


    Prediction of COVID Infection using reported symptoms

      [ Notebook (DIY) ] [ Notebook (Soln.) ]

    Based on: Zoabi et al. "Machine learning-based prediction of COVID-19 diagnosis based on symptoms." npj digital medicine 4.1 (2021): 1-5.
    Task: Predict COVID-19 infection from reported symptoms
    Dataset: English translation of COVID infections reported by Israeli Ministry of Health
    Learning objectives:
    • Explore a realistic dataset and prepare it for building a practical machine learning system
    • Think through various practical issues related to deploying such a system (e.g., class imbalance, data collection bias)
    • Build and train such an ML system addressing the issues discovered above