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1. Reinforcement_Learning-Python Reinforcement_Learning-Python Public

   We use python software and NumPy library to implement the Q-learning method , train an Agent to solve a Reinforcement Learning Problem

   Jupyter Notebook

2. Neural_Networks Neural_Networks Public

   Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural network…

   R

3. Support_Vector_Machines-vs-Discriminant_Analysis Support_Vector_Machines-vs-Discriminant_Analysis Public

   Comparing Classification Methods. We will code some Discriminant Analysis Methods and compare them to Support Vector Machines (SVMs).

   R

4. Gradient_Boosting-vs-SuperLearner Gradient_Boosting-vs-SuperLearner Public

   Implementing Gradient Boosting & SuperLearner in R and compare the classification accuracy of the two methods.

   R 1

5. Random_Forest Random_Forest Public

   Tuning Random Forests, comparing their classification accuracy to Regression Trees.

   R

6. LASSO-vs-LeastAngleRegression LASSO-vs-LeastAngleRegression Public

   Code of the least angle regression solution path by hand for an example( p=5). Then we compute the solution path for a dataset and compare it with the LASSO path.

   R