Supervisor's review on the bachelor's thesis of the 4th year student of the Department of Informatics of the Mathematics and Mechanics Department, Alimpieva A.V.  Methods of deep machine learning for detection of anomalies in X-ray images  Bachelor's work of Alimpieva is devoted to the problem of finding anomalies on chest X-ray images. The task is actual due to the shortage of qualified radiologists for the primary description of the pictures in a massive regular population survey, which is the main method of early detection of tuberculosis, other chronic infectious diseases and oncological diseases of the chest.  The aim of the work was to implement and compare methods of deep machine learning for the classification of normal images and images with pathologies on existing open sets of chest X-ray images.  The following results are achieved in the work: • Trained a neural network with the architecture of U-Net to identify areas of interest; • Learned the neural network GoogLeNet, Inception V3, ResNet; • The quality of classification is compared, all classifiers are of close quality, the best value is F1 = 0.8, which is close to published quality;  The achieved results demonstrate good repeatability of known measurements of the quality of classification and their small dependence on the architecture of the neural network. The results are of great practical importance for the choice of the network architecture in solving the problem of finding anomalies.  The work has also limitations: • F1 measure is an inadequate task of finding anomalies. For practical use, an unbalanced measure is required that must be taken into account in training, which was not done; • the results are not compared without choosing the area of ​​interest; • the text of the work uses a link to Wikipedia • the text of the thesis note has some stylistic shortcomings.  Despite the shortcomings, given the valuable practical results of the work, I believe that the bachelor's thesis of Alimpieva A.V. deserves an evaluation - "EXCELLENT".  PhD., senior lecturer of Department of Informatics Faculty of Mathematics and Mechanics of St. Petersburg State University Salischev S.I. 05/15/2018