Implement the linear regression model and train it by using gradient descent with mean absolute error and mean square error as the objective function, respectively.
Implement Fisher’s linear discriminant, then train your model on the provided dataset, and evaluate the performance on testing data.
Implement the decision tree and random forest algorithm.
Implement the cross-validation and grid search by using only NumPy, then train the SVM model from scikit-learn on the providing dataset and test the performance.
Implement the deep neural network by any deep learning framework, e.g. Pytorch, TensorFlow, or Keras, then train the DNN model by the Cifar-10 dataset.