Bachelor 2020/2021

Machine Learning with Python

Type: Elective course (Logistics and Supply Chain Management)

Area of studies: Management

Delivered by: Department of Operations Management and Logistics

Where: Graduate School of Business

When: 3 year, 4 module

Mode of studies: distance learning

Instructors: Yulia A. Kuznetsova, Maxim Rozhkov

Language: English

ECTS credits: 9

Contact hours: 30

Course Syllabus

Abstract

The course focuses on practical application of machine learning methods and tools. All classes are conducted in a computer lab and include a brief review of the necessary theoretical principles, their software implementations as well as examples of application. The course should be taken after studying the basics of Python programming, data manipulation and data representation in Python.

Learning Objectives

• The aim of the course is to introduce the learner to machine learning methods and tools and their application in management.

Expected Learning Outcomes

• Knows basic machine learning concepts, tasks, and applications in management.
• Chooses appropriate machine learning method to solve a particular problem.
• Applies specific supervised machine learning algorithms in Python with scikit-learn.
• Knows the strengths and weaknesses of particular supervised learning methods.
• Uses evaluation metrics to evaluate supervised machine learning models.
• Chooses the right metric for selecting between models or for doing parameter tuning.
• Applies specific unsupervised machine learning algorithms in Python with scikit-learn.
• Evaluates clustering and interprets clustering results.

Course Contents

• Introduction to machine learning
  Basic machine learning concepts, tasks, and applications in management. Supervised and unsupervised machine learning. Basic machine learning workflow. Python machine learning libraries.
• Supervised Learning
  Classification and regression tasks. Generalization, overfitting, and underfitting. Supervised machine learning algorithms. K-nearest neighbors. Linear models. Naive bayes classifiers. Decision trees. Ensembles of decision trees. Kernelized support vector machines. Neural networks.
• Model evaluation and selection
  Cross-Validation. Model accuracy and model evaluation metrics. Confusion matrix. Precision-Recall and ROC curves. Decision threshold. Model selection using evaluation metrics.
• Unsupervised learning
  Types of unsupervised learning. Preprocessing and scaling. Clustering algorithms. K-means. Agglomerative clustering. Hierarchical clustering. DBSCAN. Clustering evaluation.

Assessment Elements

• Assignments
• Final Examination
  Online test

Interim Assessment

• Interim assessment (4 module)
  0.4 * Assignments + 0.6 * Final Examination

Bibliography

Recommended Core Bibliography

• Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)
• Плас Дж. Вандер. Python для сложных задач: наука о данных и машинное обучение. - Санкт-Петербург : Питер, 2018. - 576 с. - ISBN 978-5-496-03068-7. - URL: https://ibooks.ru/bookshelf/356721/reading (дата обращения: 12.10.2020). - Текст: электронный.

Recommended Additional Bibliography

• Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293