/Coursework-Advanced-Machine-Learning

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Coursework-Advanced-Machine-Learning

1. A Hyperparameter Selection for Image Classification

  • Task: Building models for image classification, from scratch
    • KNN, SVMs, Decision Trees, Random forests
  • Experiments on CIFAR-10, CIFAR-100 dataset
    • Accuracies and runtime for both tarning and testing
      • w.r.t. a varying number of train data points
    • Hyperparameter search with CV

2. Multi-output Gaussian Process Regression

  • Task: Building models for regression, from scratch
    • Linear and nonlinear GP regression (vector-valued model)
    • i.i.d. Gaussian noise model
    • Isotropic Gaussian kernel (for nonlinear)
  • Experiments on SARCOS dataset
    • Accuracies and runtime
      • Accuracy surface w.r.t. varying hyperparameters
    • Computationally efficient GP regression using the subset of regressors (SOR) approximation
    • Comparisons with other regression algorithms
    • Other kernels

3. A Review on Neural Processes

  • Task: Writing a survey paper on Neural Processes
  • Academic publications discussed in the paper:
    • Neural Processes(NPs)
    • Conditional Neural Processes(CNPs)
    • Attentive Neural Processes(ANPs)
    • Sequential Neural Processes(SNPs)
    • The Functional Neural Process(FNPs)

4. A Project on Hand Pose Estimation

  • Task: Performing neural network regression algorithms on 3D Hand Pose Estimation,
  • Experiments on the 3D Hand Pose Estimation data
    • Compare models
      • RF, GP, MLP, CNN, and more
    • Accuracies and run-times
    • Details of hyperparameter selection
    • Ablation studies