Course - Introduction to Machine Learning - TDT4172
Introduction to Machine Learning
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.
Compulsory assignments
- Mandatory assignments
Further on evaluation
The re-sit examination is held in August.
If there is a re-sit examination, the examination form may be changed from written (multiple choice) to oral.
Specific conditions
Admission to a programme of study is required:
Applied Physics and Mathematics (MTFYMA)
Automation and Intelligent Systems - Engineering (BIAIS)
Chemical Engineering and Biotechnology (MTKJ)
Civil Engineering (MTBYGG)
Computer Science (MIDT)
Computer Science (MTDT)
Computer Science - Engineering (BIDATA)
Cyber Security and Data Communication (MTKOM)
Cybernetics and Robotics (MITK)
Cybernetics and Robotics (MTTK)
Digital Infrastructure and Cyber Security (BDIGSEC)
Digital Infrastructure and Cyber Security (MSTCNNS)
Electrical Engineering (BIELEKTRO)
Electrification and Digitalisation - Engineering (BIELDIG)
Electronic Systems Engineer - Engineering (BIELSYS)
Electronics System Design and Innovation (MTELSYS)
Energy and the Environment (MTENERG)
Engineering and ICT (MTING)
Industrial Design Engineering (MTDESIG)
Industrial Economics and Technology Management (MTIØT)
Informatics (BIT)
Informatics (MSIT)
Logistics - Engineering (FTHINGLOG)
Marine Technology (MTMART)
Mathematical Sciences (BMAT)
Mechanical Engineering (MIPROD)
Mechanical Engineering (MTPROD)
NTNU School of Entrepreneurship (MIENTRE)
Natural Science with Teacher Education, years 8 - 13 (MLREAL)
Physics (MSPHYS)
Recommended previous knowledge
Basci programming knowledge in Python.
Statistics, corresponting to ST1101/TMA4240/TMA4245
Linear algebra, corresponding to TMA4110/TMA4115/MA1201
Calculus, corresponding to TMA4105, MA1103
Course materials
Hands-on Machine Learning with Scikit Learn, Keras and Tensorflow, 2022, Aurelien Geron
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| BBAN3001 | 3.5 sp | Autumn 2025 |
| IDATG2208 | 7.5 sp | Autumn 2025 |
Subject areas
- Computer and Information Science
Contact information
Course coordinator
Department with academic responsibility
Examination
Examination
Ordinary examination - Autumn 2025
School exam - multiple choice
The specified room can be changed and the final location will be ready no later than 3 days before the exam. You can find your room location on Studentweb.