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data/home/content/education/kotlin.md
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Recording of lectures in 2019 is available [here](https://www.youtube.com/playlist?list=PL4_hYwCyhAvZzRpbK4iTy9S6_OWZNEiVk).
## Lecturer
[Alexander Nozik](${resolvePageRef("team")#nozik})
## Course purpose
As physics (and science in general) develops, computer methods are becoming more and more important in the daily work of a scientist. In conducting an experiment, computer methods and tools are used at all stages of the work: planning the experiment, preparing the installation, collecting data, processing and publishing it. In such a situation, the quality of the programs used is beginning to play an important role. In addition, there is a need for specialists who understand both science and programming and who develop and improve software tools.
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Most students (and scientists) are more or less familiar with the basic tools of a programmer, for example, writing simple programs in Python. This is not enough for serious scientific development, so the course aims at a more advanced understanding of hardware, program structure and modern development tools.
As the main programming language we will use `Kotlin`, which appeared recently and managed to gain a large market share. Kotlin has several significant advantages as an initial language for advanced scientific programming:
* Strict typing, a clearly constructed system of types.
* High performance.
* Automatic memory management.
@ -24,13 +26,6 @@ As the main programming language we will use `Kotlin`, which appeared recently a
* Extensive community.
* Possibility of commercial use.
## Lecturer
[Alexander Nozik](https://www.researchgate.net/profile/Alexander_Nozik) - experimental physicist, data analysis specialist in physical experiment and scientific software.
Senior researcher at the INR RAS, Deputy Head of the MIPT LNPM. [JetBrains Research](https://research.jetbrains.org/groups/npm/) team leader.
[Andrey Shcheglov](https://www.linkedin.com/in/andreyshcheglov/?locale=en_US) - Senior Software Engineer at JetBrains.
## Course format
In 2020, the course is held with the participation of JetBrains and the support of JetBrains Research. The most active students will have the opportunity to participate in summer internships at JetBrains. There is also an opportunity for senior students to do research at the MIPT Laboratory of Nuclear Physics Experiments Methods (participant of JetBrains Research) and at the JetBrains Moscow office.
@ -42,7 +37,6 @@ All questions related to the course will be discussed in telegram groups [@mipt-
All those wishing to participate should complete the [form](https://docs.google.com/forms/d/e/1FAIpQLSeNZT8B90pT6fM9oABHFbrtv6pKfoYKfO-ANAjLlgWynMnh_g/viewform).
## Course content
1. **From hard to soft**
1. Program as a set of instructions. Evolution of programs.
2. Memory structure. Segmentation fault.
@ -52,7 +46,7 @@ All those wishing to participate should complete the [form](https://docs.google.
6. Static and dynamic linking. Libraries.
7. Program structure. Entry points.
2. **The tools of the modern programmer**
1. Automatic assembly systems.
1. Automatic build systems.
2. Version control systems.
3. Integrated development environments.
3. **Kotlin language**

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[Materials](https://github.com/mipt-npm-study/stat-methods)
## Course structure (preliminary program)
1. **Statistical decision-making theory.**
1. Decisions in deterministic tasks.
2. Decisions in non-deterministic tasks, risk function.
3. Conditional probability, decision making strategies.
3. Conditional probability, decision-making strategies.
2. **Basic concepts of probability theory.**
1. Definitions of probability.
2. Function of plausibility.
3. Point and interval estimates of distribution parameters.
4. Confidence intervals.
3. **Errors in physical experiment.**
1. Statistical and systematic errors.
2. Properties of distributions at replacement of variables.
3. Uncorrector stacking.
4. Adding results of various experiments.
4. **Properties of distributions.**
1. Poisson's binomial distribution and distribution.
2. Normal distribution and its properties.
3. Average values, moments of distributions.
5. **Checking statistical hypotheses.**
1. Functions of random variables.
2. Statistical criteria and their properties.
3. Methods of criteria construction.
4. Criteria of data agreement with the theory.
6. **Evaluation of parameters.**
1. Parameter criteria.
2. Maximum probability and chi-square method.
3. Using the probability function to construct the Chi-square maximum and Chi-square maximum. Interval estimates.
4. Interval estimates in the case of normal distribution.
7. **Modern data analysis methods (optional).**
1. Fitting of experimental curves. Criteria of phytate quality. Computer methods for solving optimization problems.
2. Multiparameter analysis. Analysis of correlations.
2. Multi-parameter analysis. Analysis of correlations.
3. Fisher Information and its Application. Maximum information and its application.
the border between Rao and Kramer.
4. Two approaches to probability: frequency approach and subjective probability. The problem of unique events.
@ -73,7 +66,5 @@ The test takes place in the form of a presentation based on the materials of an
* The main textbook for the course - W. Idieu, D. Dryard, F. James, M. Ruth, B. Sadule.
*Statistical methods in experimental physics* M.: Atomizdat, 1976. The Russian-language edition of the book is a bibliographical rarity, but the English version is republished every few years. In addition, an electronic version of the Russian-language edition is available (including the course materials on Google-drive).
* A lot of useful information is contained in the introductory chapters to the MIPT laboratory workshop for the 1st and 3rd courses.
* In concentrated form, information on probability theory and mathematical statistics can be found in the online version of the Particle Data Group (PDG) handbook of particle physics: <http://pdg.lbl.gov/2014/reviews/rpp2014-rev-probability.pdf>; <http://pdg.lbl.gov/2014/reviews/rpp2014-rev-statistics.pdf>.