DS211
Intro to Machine Learning
Faculty
Anna Aksenova
Senior Data Scientist at EPAM Systems
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
This course is your first step into the world of data science. We’ll cover all the essential steps for building a machine learning algorithm, from data processing to problem formulation, metric and algorithm selection, and hyperparameter optimization. Topics include classification, regression, outlier detection, and clustering. For each algorithm, we’ll discuss its pros, cons, and best use cases. At the end of the course, students will get the chance to solve their own machine learning problem.
Learning highlights
- A solid understanding of the foundational steps in building machine learning models.
- Good command of data preprocessing techniques associated with different ML algorithms.
- The ability to select appropriate metrics and algorithms for various machine learning tasks.
- Knowledge of key machine learning techniques: classification, regression, outlier detection, and clustering.
- Insights into the strengths, limitations, and practical use cases for different machine learning algorithms.
Course outline
15 classes
Session 1
Course practicalities discussion. Intro to machine learning, what is it about? Numpy pandas intro.
Session 2
Data is the first word of Data Science. Data types, data preprocessing.
Session 3
Train/test split. Regression. Linear regression. Quality metrics. Multicollinearity issue.
Session 4
Classification. Logistic regression. Quality metrics.
Session 5
Variance-bias tradeoff. Overfitting/underfitting. Cross-validation. Regularisation.
Session 6
Classification metrics continued. What if we have more than 2 classes?
Session 7
Tree-based methods. Decision trees for classification and regression.
Session 8
Ensembles. Random forest, bagging, boosting.
Session 9
KNN. Curse of dimensionality. Outlier detection.
Session 10
Clustering. K-Means, Agglomerative clustering, DBSCAN. Quality metrics.
Session 11
Course recap. How to choose your model?
Session 12
Exam. Start of project work
Session 13
Project work.
Session 14
Project work.
Session 15
Project presentations.
Course materials
Media
Prerequisites
Strong Python.
Basic knowledge of linear algebra and calculus.
Basic understanding of functions, their derivatives and gradients.
Basic knowledge of pandas and numpy would be a plus.
Methodology
Each class lasts 3 hours. Each class will start with a 5-minute theory quiz based on the previous day, then will continue with ~1h lecture and then will be concluded by a practical session. Students will also have home assignments every 2-3 days, a pen-and-paper exam and a small project at the end of the course.
Grading
Anna Aksenova is a Machine Learning and NLP specialist working on enterprise-scale agentic systems and Retrieval-Augmented Generation solutions, with a focus on sales and finance domains. Alongside her industry work, she has led applied research and development in healthcare-related Horizon Europe projects. Anna holds a Master’s degree in Data Science, Machine Learning, and AI from Aalto University, where her thesis focused on training a multilingual large language model for European languages. She teaches Machine Learning and NLP courses at both university and corporate levels and supervises graduate students’ research projects.
See full profileApply for this course
Intro to Machine Learning
by Anna Aksenova
Total hours
45 Hours
Dates
May 18 - Jun 05, 2026
Fee for single course
€1500
Fee for degree students
€750
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FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.