aidinattar / locp_modb

Exercises of the Laboratory of Computational Physics Mod. B

CPL_MODB

Exercises of the Laboratory of Computational Physics Mod. B course A.Y. 2021/2022.

Group: Amjadi Bahador, Attar Aidin, Joulaei Vijouyeh Roya, and Roshana Mojtaba

Prof. Marco Baiesi

Week 1

Optimization techniques for Machine Learning:

• Vanilla Gradient Descent (GD);
• Gradient Descent with Momentum (MGD);
• Nesterov accelerated Gradient (NAG);
• RMSProp
• Adams

Week 2

Deep Neural Networks

Use of DNNs for binary classification in 2D with Keras:

• Behaviour of the DNN training and validation curves as a function of the number of samples used;
• Estimation of the best hyper-parameters using a GridSearchCV algorithm;
• Behaviour of the DNN training and validation curves as a function of the rescaling and the inititial weigths
• Repeating the procedure with a more complex distribution

Week 3

Convolutional Neural Networks

Use of CNNs for pattern recognition with Keras:

• Behaviour of the CNN training and validation curves as a function of the parameters such as the amplitude A;
• Behaviour of the curves varying the regularization factor and function
• Finding the best model for our task.

Week 4

XGBoost trees

Use of XGBoost trees for pattern recognition and binary classification:

• Comparison with the results of CNN;
• Feature extraction using 'tsfresh';
• Combination of the feature extraction with a CNN;
• Behaviour of the accuracy as a function of the parameters used
• Study of the best simple XGBoost model keeping a good accuracy.

Week 5

DBSCAN and t-SNE

Use of DBSCAN and t-SNE for visualization and clustering:

• The role of dimensions;
• The role of perplexity;
• Tuning of the parameters in DBSCAN algorithm for clustering
• t-SNE for clustering