Machine Learning with Python

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Образовательная программа «Управление цифровым продуктом»

Статус:

Курс обязательный

Когда читается:

2-й курс, 3 модуль

Преподаватель

Поляк Марк Дмитриевич

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

• • To introduce students to basic concepts of data analysis and machine learning.
• • To develop practical skills of using Python to solve machine learning problems.

Expected Learning Outcomes

• • Learn to explore and analyze data with Python.
• • To familiarize students with modern tools such as Git & GitHub, Jupyter notebooks, Cloud computation.
• • To know machine learning capabilities and limitations.
• • To provide experience training models for classification, regression and clustering tasks using Python machine learning libraries.
• • To understand pros and cons of popular machine learning algorithms.

Course Contents

• • Regression
• • Introduction and basic concepts
• • Classification
• • Model evaluation
• • Artificial neural networks
• • Unsupervised learning
• • Deep learning
• • Natural language processing
• • Recommender systems

Assessment Elements

• Final Exam
• Quizzes
• Home assignments

Interim Assessment

• 2021/2022 3rd module
  0.1 * Quizzes + 0.4 * Final Exam + 0.5 * Home assignments

Bibliography

Recommended Core 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.

Recommended Additional 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. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822
• 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.