--------------------------------------------- Machine Learning Algorithms ---------------------------------------------

Description of each directory:

Classification: Scripts to classify different events. These are used to discriminate between muons and electrons in the ANNIE experiment. Project includes: script1 to compare the performance of different classification algorithms (accuracy/ROC curves), script2 for the optimisation of each algorithm parameters, script3 to plot the normalised confusion matrix.

Clustering: Scripts to find the number of clusters - These are to be used to find the number of observed rings in the ANNIE experiment.

DSG_Turing: example scripts used for the NATS project (Data Study Group - Alan Turing Institute) to predict the aircraft trajectory using a ParticleFilter. See code.

EnergyReconstruction (Regression projects): Scripts used to predict the track length and the particle energy in water Cherenkov detectors.

• For the track length reconstruction in the ANNIE experiment we use a Deep Learning Neural Network from Tensorflow: See code1 for training, code2 for prediction, code for optimisation, example data and paper
• For the muon/neutrino energy reconstruction in the ANNIE experiment we use a BDTG from Scikit-Learn. See code1, code2, example data and paper. Such code can be trained in a different step. In this case, we train the algorithm and store the weights using script1 and we make the prediction using the existing weights and script2. To optimise the training parameters use script.
• Developed a new generic method to reconstruct the incident particle energy from observable data in Water-Cherenkov detectors. For this project, the BDTG from Scikit-Learn was found to show the best performance as documented in this paper/JINST 13 P04009. See code. The different codes that were tested: the gradient BDT algorithms from the Scikit-Learn 0.18.2 and TMVA packages (ROOT 5.34/23), a multi-layer percepton Neural Network (NN) from the TMVA package and a multi-layer NN implemented using TensorFlow (TNN) via the Keras 1.2.2 library in Python can be found here.

mini_projects: mini projects for NLP, Speech and Time Series analysis.