Обсуждение:Численные методы обучения по прецедентам (практика, В.В. Стрижов)/Группа 174, весна 2014

Материал из MachineLearning.

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My first research paper

The course involves Experts, Assistants and Sutudents of the Moscow Institute of Physucs and Technology.


A student is willing to learn to formally state research problems, find adequate references, generate novel and significant ideas for problem solving.

An assistant helps the student with technical issues, consults the student on machine learning topics, promptly reacts to arising problems, performs evaluations and grading. Each advisor is supposed to possess sufficient publishing experience. Ideally, the assistant is writing paper on the adjacent topic. It is recommended to organize weekly reviewing process in such way that a student would input the corrections himself/herself.

An expert guarantees novelty and importance of the paper, suggests the problems, provides data.


Author Research title Link Assistant HW-1,2 Code Score Grade
Gazizullina Rimma Capacity of railway cargo transportation forecasting [1], pdf Stenina Maria \frac{15}{15}+\frac{10}{16} [MF]TAI+L+SBR+CV+T>DEH(J) 16 10
Grinchul Andrey Struture Learning [2], pdf Varfolomeeva Anna \frac{7}{15}+\frac{2}{16} [F]TA+I+LSBR+СV+T+D+E(F) 14,5 9
Guschin Alexander Inductive model geneneration in information retrieval [3], pdf Kuznetsov Mikhail \frac{5}{15}+\frac{2}{16} [F]TAI+L+SBRCVTDEHS(F) 15,5 9
Efimova Irina Differential diagnosis (ECG) [4], pdf Celyh Vlada \frac{15}{15}+\frac{12}{16} [MF]T+A+I+L+SB++R+CV+TDE+H(J ed) 17,25 10
Zhukov Andrey Robust university ranking [5], pdf Kuznetsov Mikhail \frac{8}{15}+0 [F]TAIL+SBRCVTDEHS(F) 15,25 9
Ignatov Andrey MAbifold learning for quasiperiodic time series forecasting [6], pdf Ivkin Nkita 0+\frac{7}{16} [MF]TA+I+L+S+B+R+C+VTD>E+HS (J if ed) 18 10
Karasikov Mikhail Dimensionality reduction for multi-class learning problems reduced to multiple binary problems [7], pdf Yu.V. Maximov 0+0 [MF]TAI+L+SBRC+V+TDESH(J) 15 10
Kulunchakov Andrey Detection of isomorphic structures in essentially nonlinear regression models [8], pdf Sologub Roman, Kuznetsov Mikhail \frac{10}{15}+\frac{14}{16} [F]T+AI+L+S+BR+CVT++D+EHS(J ed-ed) 17 10
Lipatova Anna Simultaneous Clustering Of A Set Of Time Series And Corresponding Forecasting Models [9], pdf Motrenko Anastasia \frac{8}{15}+\frac{6}{16} [MF]TA+I+LSBR-CVTDE (J when ed) 14,25 10
Makarova Anastasia Nonlinear forecasting [10], pdf Motrenko Anastasia 0+0 [F]TAI-LSB+R-CVTD>E>(F) 12,75 9
Plavin Alexander Number of topics optimization in probabilistic topic models by sparsing regularizer [11], pdf Potapenko Anna \frac{13}{15}+\frac{14}{16} [F]T+A+I+L+S+BR++CVTD+>>(?) 14 10
Popova Maria Selection of optimal physical activity classification model using measurements of accelerometer [12], pdf Tokmakova Alexandra \frac{11}{15}+\frac{6}{16} [MF]T+AI+L++SB++R+CV+TD+(JV ed) 15,25 10
Shvets Mikhail Multimodel interpretation foe sociologic data [13], pdf Aduenko Alexander \frac{11}{15}+\frac{4}{16} [M+F]T+A+I+L+S+B+R+CVTD+E(F) 16,25 9
Shinkevich Mikhail On regularization and model robustenss [14], pdf Dudarenko Marina \frac{15}{15}+\frac{9}{16} [MF]T+AIL+S+BR+CV+T+D+E+H(J ed) 17 10
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