The lab1 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• knn.py which is the provided script to modify for building a k-nearest-neighbors predictor model
• mnist.py which is the provided script to load the mnist dataset for training/testing
• zeroR.py which is the provided script containing a predictor model
• report.py which is my submitted report for this assignment

1. Modify the dir variable in the knn.py script to direct the path to your downloaded datasets.
2. Run the mnist.py code first.
3. Run knn.py. See datasets for any additional files required to run the program.

The dataset used for this program is the MNIST dataset and the Solar particle dataset, as provided by the instructor on the course webpage.

Experimental results for this assignment can be found in this Google sheets* document, in the lab1-knn sheet.
*This document may not be available after the course end date.

The lab2 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• magic04.txt which is the provided dataset
• decision_tree.py which is the provided script to modify for building a decision tree classification model
• regression_tree.py which is the provided script to modify for building a decision tree regression model

1. Modify the dir variable in the regression_tree.py program to direct the path to your solar particle dataset.
2. Compile the decision_tree.py program or the regression_tree.py program, or both to your preference.

The dataset used for this program is provided by the instructor on the course webpage.

Experimental results for this assignment can be found in this Google sheets* document, in the lab2-dectree sheet.
*This document may not be available after the course end date.

The lab3 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• decision_tree.py which is the provided script to modify for building a decision tree classification model
• regression_tree.py which is the provided script to modify for building a decision tree regression model

1. Modify the dir variables in the regression_tree.py and decision_tree.py programs to direct the path to a dataset of your choice.
2. Compile the decision_tree.py program or the regression_tree.py program, or both to your preference.

The dataset used for this program is provided by the instructor on the course webpage.

Experimental results for this assignment can be found in this Google sheets* document, in the lab2-dectree sheet.
*This document may not be available after the course end date.

The lab4 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• __init__.py which is the main script to compile the program
• dataset.py which contains the Dataset class that loads and stores the datasets for use in the program
• dectree.py which contains the classification and regressor predictor models for decision trees
• forest.py which contains the classification and regressor predictor models for forests
• knn.py which contains the classification and regressor predictor models for knn
• logreg.py which contains the classification and regressor predictor models for logistic regression
• svm.py which contains the classification and regressor predictor models for support vector machine
  For information about the other files in this directory, see the Results section below.

1. Modify the dataset.py script to access the dataset(s) of your choice
2. Compile the __init__.py program, or both to your preference.

The datasets used for this program are provided by the instructor on the course webpage.

Experimental results for this assignment can be found in the res.txt and results.txt files included in the lab4 directory.

The lab5 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• __init__.py which is the main script to compile the program
• cnn.py which contains the code to develop and test convolutional neural networks on two datasets: MNIST and CIFAR-10
• dnn.py which contains the code to develop and test fully connected dense neural networks on two datasets: solar particle and gamma ray For information about the other files in this directory, see the Results section below.

1. Modify the dataset.py script to access the dataset(s) of your choice
2. Compile the __init__.py program, or both to your preference.

The datasets used for this program are either provided by the instructor on the course webpage (solar particle and gamma ray) or imported via the keras library (MNIST and CIFAR-10).

Experimental results for this assignment can be found in the lab5\lab5.txt file or, for specific runs, in the respective lab5\results directory containing test##.txt files.

The lab6 directory contains the following files:

• _instr.pdf which contains the instructions for the assignment
• __init__.py which is the main script to compile the program
• results-base.txt which contains the accuracy results of the predictions by the baseline model
• results-lstm.txt which contains the accuracy results of the predictions by the LSTM model
• results-conv.txt which contains the accuracy results of the predictions by the convolutional model For information about the other files in this directory, see the Results section below.

1. Modify the dataset.py script to access the dataset(s) of your choice.
2. Compile the __init__.py program.

The datasets used for this program are either provided by the instructor on the course webpage (solar dataset: xrp.npy).

Experimental results for this assignment can be found in the npy files. Accuracy results are presented in the results-XXXX.txt files.

The project directory contains the following files:

• baseline.py which contains the code to build and train the baseline model
• main.py which contains the code to build and train the Naive-Bayes classifiers
• doc2vec.py which contains the code to build a Doc2Vec embeddings for the dataset
• dataset.py which contains the code to read and load the dataset

1. For the first run (in both baseline.py and main.py), modify the constructor to Dataset to contain first_time=False. This will load the data in your system (make sure d in dataset.py reflects your system configuration) and create npy files for NumPy to use in future runs.
2. In the second run (in both baseline.py and main.py), remove the first_time=False modification we used in (1). Then, change the main method to call second_run(...).
3. In any future runs (in both baseline.py and main.py), comment out the line of code in (2), and run as normally.