Bachelor
2023/2024
Machine Learning with Python
Type:
Compulsory course
Area of studies:
Business Informatics
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
2 year, 4 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Yury Sanochkin
Language:
English
ECTS credits:
4
Contact hours:
48
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
- Home assignmentsHome assignments for the relevant topics discussed in the class.
- 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.
- Team projectStudents divide into teams of 3-4 people. The teams have to solve ML task in a competitive way.
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.