This repo contains homework assignment solutions for the Stanford CS 330 (Deep Multi-Task and Meta Learning) class offered in Autumn 2023. A brief summary of key concepts covered in different assignments is summarized below.

In this assignment, we will implement a multi-task movie recommender system based on the classic Matrix Factorization and Neural Collaborative Filtering algorithms. In particular, we will build a model based on the BellKor solution to the Netflix Grand Prize challenge and extend it to predict both likely user-movie interactions and potential scores. A multi-task neural network architecture is implemented and the effect of parameter sharing and loss weighting on model performance is explored.

The main goal of these exercises is to get familiar with multi-task architectures, the training pipeline, and coding in PyTorch.

Goal of this assignment is to understand meta-learning for few shot classification. Following are the key undertakings:

• Learn how to process and partition data for meta learning problems, where training is done over a distribution of training tasks
• Implement and train memory augmented neural networks (MANN), a black-box meta-learner that uses a recurrent neural network [1].
• Analyze the learning performance for different size problems.
• Experiment with model parameters and explore how they improve performance. The analysis is performed using Omniglot [2], a dataset with 1623 characters from 50 languages and each character has 20 images.

[1] Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016, June). Meta-learning with memory-augmented neural networks. In International conference on machine learning (pp. 1842-1850). PMLR.

[2] Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338.