Бакалавриат
2022/2023
Машинное обучение на языке Python
Статус:
Курс обязательный (Цифровые инновации в управлении предприятием)
Направление:
38.03.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Бугаевский Владимир Михайлович,
Гончаренко Владислав Владимирович
Язык:
английский
Кредиты:
4
Контактные часы:
36
Course Syllabus
Abstract
It is said that automation increases productivity, which in turn drives global economy and helps to improve quality of life. For the last decade machine learning remains one of the key sources of automation in nearly all industries. This course familiarizes students with modern machine learning algorithms by both providing theoretical basis and hands-on experience with Python libraries.
Learning Objectives
- The course is aimed to provide students with necessary knowledge and tools to work with machine learning tasks.
- During the learning process, students will gain the ability to develop real ML projects in teams of 3-4 people.
Expected Learning Outcomes
- Be able to set up python environment for ML task;
- Understand key concepts of ML, current trends of AI;
- Be able to pass through all steps of DS task: EDA, process missing data and outliers, train an ML model, evaluate an ML model;
- Be able to find and read articles about ML applications.
Course Contents
- 1. Introduction to machine learning. Types of ML tasks and model classes.
- 2. Metrics. KNN. Naive Bayes
- 3. Regression. Linear Regression
- 4. Classification.
- 5. Trees. Ensemble of tries
- 6. Introduction into Deep Learning
- 7. NLP and DL
- 8. Unsupervised ML algorithms
- 9. Recommender Systems
Assessment Elements
- Team projectStudents divide into teams of 3-4 people. The teams have to solve ML task in a competitive way.
- QuizzThe written quiz. Is conducted in a workshop at the end of the module. Each correct answer gives a certain number of points. The final grade is calculated as the sum of the points received for the correct answers, then normalized (1-10) and rounded to the nearest integer.
- Home assignmentsHome assignments for the relevant topics discussed in the class.
Bibliography
Recommended Core Bibliography
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
- McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
- Pattern recognition and machine learning, Bishop, C. M., 2006
Recommended Additional Bibliography
- Aurélien Géron. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems: Vol. Second edition. O’Reilly Media.
- Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.