M.Sc. Courses in Data Science, including Machine Learning, Deep Learning, Statistics and Data Analysis, and Recommendation Systems.

• Machine Learning from Data assignments, cover Linear Regression, Decision Tree, Naive Bayes, Logistic Regression, EM, KNN, K-means, and additional basic theory knowledge of SVM, Perceptron, PAC Learning, VC Dimension, and Dimensionality Reduction.

• Deep Learning assignments, cover CNN (Convolutional layers, Pooling, Batch Normalization, Dropout regularization, etc.), Sequence modeling (RNN), Attention (Encoder-Decoder and Latent space), and Generative models (GAN).

• Statistics and Data Analysis assignments, performing statistics computations on research data and inferring significant conclusions. Includes hypothesis models (p-values), correlations, confidence intervals, False discovery rate (FDR) corrections, distributions, etc.

• Recommendation Systems assignments, cover Collaborative Filtering, Content-Based, Matrix Factorization (MF), Multi layer perceptron (MLP), Generalized Matrix Factorization (GMF), Neuro Matrix Factorization (NMF).