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TDT4172

Introduction to Machine Learning

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New from the academic year 2024/2025

Credits 7.5
Level Third-year courses, level III
Course start Autumn 2024
Duration 1 semester
Language of instruction Norwegian
Location Trondheim
Examination arrangement School exam - multiple choice
Special deadlines for course registration
Autumn: 2024-06-01

About

About the course

Course content

The course gives a basic introduction to data analysis and machine learning. It covers the learning regimes of supervised and unsupervised learning thoroughly, and a light introduction to reinforcement learning and explanation methods for machine learning models. The course work is project driven with focus on applications, using Python and commonly used machine learning libraries.

Learning outcome

Knowledge: Fundamentals of machine learning with commonly used learning algorithms. Skills: Ability to analyse data sets, and train and evaluate machine learning models on data. Evaluate adequateness of learning regimes based on the data. General competencies: Understand the basic principles of data analysis and machine learning. Knowledge about the applicability and limitations of different contemporary learning algorithms.

Learning methods and activities

Lectures, self-study. Compulsory activity in the form of assignments, will be published during the semester. These must be passed to gain admittance to the final exam.

Restricted admission: This course is for 500 students only.

Compulsory assignments

  • Mandatory assignments

Further on evaluation

Admission restriction: Only 500 students will be admitted. You must apply via Studentweb. Students who have the course as mandatory or elective in their study plan will be prioritized first.

If there is a re-sit examination, the examination form may be changed from written (multiple choice) to oral.

Course materials

Hands-on Machine Learning with Scikit Learn, Keras and Tensorflow, 2022, Aurelien Geron

Subject areas

  • Computer and Information Science

Contact information

Course coordinator

Department with academic responsibility

Department of Computer Science