Machine Learning
Successful organisations or companies like Google and Amazon have invested huge amounts in machine learning, which they use to analyze their customers’ interests and behaviors to optimize their products, processes and marketing. In this course, you will learn to master the essential machine learning models in Matlab, Python, or R.
The course is based on the most popular machine learning course in Denmark including the textbook: “Introduction to Machine Learning and Data Mining”. The course is given in English and is taught by DTU Professor Morten Mørup and Associate Professor Mikkel N. Schmidt who have 10+ years of experience in machine learning research.
This course provides a strong intuition of different machine learning algorithms and knowledge of which methods you can apply to a given problem. Skills in specific topics including model construction (feature extraction, dimensionality reduction, cross-validation, and model selection); supervised learning (linear regression, logistic classification, decision trees, artificial neural networks, and ensemble learning); and unsupervised learning (hierarchical clustering, kernel density estimation, mixture modeling, association mining, and outlier detection). Finally, the course provides insights into the most important steps in machine learning, from data preparation, modelling, validation and presentation of the obtained results.
During the course/programme you will work with:
The theory and practical applications of the most prominent and common machine learning methodologies. Lectures will primarily cover the theories of the methods taught and exercises highlight their practical use and applications.
What is in it for you?
- A strong intuition of different machine learning algorithms and knowledge of which methods you can apply on a given problem.
- Skills in specific topics like model construction (feature extraction, dimensionality reduction, cross-validation, and model selection); supervised learning (linear regression, logistic classification, decision trees, artificial neural networks, and ensemble learning); and unsupervised learning (hierarchical clustering, kernel density estimation, mixture modeling, association mining, and outlier detection).
- Insight into the most important steps in machine learning, from data preparation, modelling, validation and presentation of the obtained results.
- Our machine learning book and access to our custom developed toolboxes in Python, Matlab, and R, which provides fast development, application and validation of the methods taught in the course.
- The possibility for certification through report work, where the methods taught in the course are applied on your own data and problems.
What is in it for your company?
Participants will upon completing the course master the essential machine learning models in Matlab, Python, or R. They will after the course work on a project within their own problem domain and complete the course by handing in the project in which they apply the machine learning methodologies learnt ideally to problems of the company. Feedback on this project work will be provided to the participants. The participant will thereby upon completing the course be able to solve machine learning problems relevant for the company.
Who is the course relevant for?
This course is for people with interest in machine learning and who is comfortable with math as taught during first year of university studies (basic linear algebra and probability theory), and is ready to use and create machine learning algorithms.
Practical information
Language
English
Duration
Five days from Monday to Friday 9.00 – 16.00
Where
On DTU Lyngby Campus in classes with up to 40 students
Teaching material
Please let us know if you prefer working in Matlab, Python, or R, so we can prepare the relevant teaching material for you.
Cancellation
In case of too few participants, we reserve the right to cancel or postpone the course.
Waiting list
In case of too many participants, we reserve the right to make a waiting list.
Deadline
Deadline for registration for the course on 13 May 2024 is 29 April 2024.
Admission requirements
Someone with an interest in machine learning and is comfortable with math as taught during first year of university studies (basic linear algebra and probability theory), and is ready to use and create machine learning algorithms.
Prerequisites:
Linear algebra as covered in this study material
Basic probability theory as covered in this study material
Basic programming skills in either Matlab, Python, or R
Questions?
Contact Morten Mørup
Professor
Questions?
Contact about registration
Course Coordinators
