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Введение в компьютерный интеллект. Машинное обучение.

Содержание

В весеннем семестре 2020 года на механико-математическом факультете МГУ им. М. В. Ломоносова начинается чтение нового спецкурса по выбору студента, посвященного классическим алгоритмам машинного обучения.

Курс будет читаться на базе кафедры Математической Теории Интеллектуальных Систем под руководством д.ф.-м.н., профессора Бабина Д. Н. Курс будут читать к.ф.-м.н. Петюшко А. А. и к.ф.-м.н. Иванов И. Е.

Курс читается по вторникам в 18:30, ГЗ МГУ, аудитория 1205 (ГЗ МГУ).

  • Telegram-канал, в котором будут появляться все важные новости
  • Обратная связь - по почте mlcoursemm@gmail.com
  • Ну и всегда можно написать в issues :)
Номер Дата Лекция Семинар
01 18.02.2020 Вводная лекция Вводное занятие по Python
02 25.02.2020 Непараметрические методы классификации и регрессии Вводное занятие по Python (продолжение)
03 03.03.2020 Вероятностный подход к классификации Разбор домашних заданий из курса по CV
04 10.03.2020 Регрессия и оценка качества Введение в pandas
05 17.03.2020 Линейные классификаторы Метрики качества классификаторов
06 24.03.2020 SVM Построение SVM
07 31.03.2020 Решающие деревья. Случайный лес Работа с пропущенными значениями. Выбор признаков
08 07.04.2020 Ансамбли Обзор площадки Kaggle и соревнование
09 14.04.2020 Ансамбли Обзор методов ансамблирования в sklearn
10 21.04.2020 Разведывательный анализ Разведывательный анализ
11 28.04.2020 Методы уменьшения размерности Разбор домашних заданий курса
  1. Курс лекций по машинному обучению на http://www.machinelearning.ru от Воронцова К. В.
  2. Hastie, T. and Tibshirani, R. and Friedman, J. The Elements of Statistical Learning, 2nd edition, Springer, 2009.
  3. Bishop, C.M. Pattern Recognition and Machine Learning, Springer, 2006.

Шпаргалки

  • Краткая справочная информация по Python, NumPy, SciPy, SciKit-learn, Pandas, MatPlotLib, Jupyter Notebook: см. в папке с документацией курса 2019 года

Введение в Python

Введение в машинное обучение

Introduction to Computer Intelligence. Machine learning.

Content

  • (2020-05-08) Uploaded 10th lecture, read by V. Korviakov and E. Kaziakhmedov.
  • (2020-04-14) Uploaded 9th lecture and seminar.
  • (2020-04-07) Competition is open on kaggle. Deadline: until May 15 inclusive.
  • (2020-04-07) Uploaded 8th lecture
  • (2020-04-01) Uploaded second practical task. Deadline for submit: until May 01 inclusive. Submitting an assignment must be done with the subject title [ML2020:prac02]
  • (2020-04-01) Uploaded 7th lectureand seminar.
  • (2020-03-24) Uploaded second theoretical task. Deadline for submit: until April 14 inclusive. Submitting an assignment must be done with the subject title [ML2020:th02]
  • (2020-03-24) Uploaded 6th lecture.
  • (2020-03-17) Uploaded 5th lecture.
  • (2020-03-11) Uploaded first practical task. Deadline for submit: until April 01 inclusive. Submitting an assignment must be done with the subject title [ML2020:prac01]
  • (2020-03-11) Uploaded 4th lecture and seminar pandas. A section with code examples has been added notebooks
  • (2020-03-04) Uploaded first theoretical task. Deadline for sending: until March 25 inclusive. Submitting an assignment must be done with the subject title [ML2020:th01]
  • (2020-03-04) Uploaded 3rd lecture
  • (2020-02-27) Uploaded 2nd lecture
  • (2020-02-19) Uploaded 1st lecture and first seminar
  • The first lecture will take place on Tuesday 18, February, at 18:30 in room 1205 (main bilding MSU)
  • This semester, in addition to lectures, we will also include seminars on which practical issues of machine learning will be discussed. Seminars will be held immediately after the lectures.

In the spring semester of 2020 at the Faculty of Mechanics and Mathematics of Lomonosov Moscow State University begins reading a new special course of the student's choice, dedicated to classical machine learning algorithms.

The course will be taught on the basis of the Department of Mathematical Theory of Intelligent Systems under the guidance of Doctor of Physical and Mathematical Sciences, Professor Babin D.N. The course will be delivered by Ph.D. Petiushko A.A. and Ph.D. Ivanov I.E.

The lessons are to be taught on Tuesdays at 18:30, main bilding MSU, room 1205.

Number Вate Lecture Seminar
01 18.02.2020 Introductory lecture Introductory Python Lesson
02 25.02.2020 Nonparametric classification and regression methods Introductory Python Lesson (Continued)
03 03.03.2020 Probabilistic approach to classification Analysis of homework from the CV course
04 10.03.2020 Regression and quality assessment Introduction to pandas
05 17.03.2020 Linear classifiers Classifier quality metrics
06 24.03.2020 SVM Building SVM
07 31.03.2020 Decision trees. Random forest Working with missing values. Feature selection
08 07.04.2020 Ensembles 1 Kaggle site overview and challenge
09 14.04.2020 Ensembles 2 An overview of ensemble methods in sklearn
10 21.04.2020 Exploratory data analysis Exploratory data analysis
11 28.04.2020 Dimension reduction methods Analysis of the course homework
  1. Machine Learning Lecture Course on http://www.machinelearning.ru from Vorontsov K.V.
  2. Hastie, T. and Tibshirani, R. and Friedman, J. The Elements of Statistical Learning, 2nd edition, Springer, 2009.
  3. Bishop, C.M. Pattern Recognition and Machine Learning, Springer, 2006.

Cheat sheets

  • Quick reference information on Python, NumPy, SciPy, SciKit-learn, Pandas, MatPlotLib, Jupyter Notebook: see in document folder of course 2019 year

Introduction to Python

Introduction to machine learning

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