course-details-portlet

TDT4172 - Introduction to Machine Learning

About

New from the academic year 2024/2025

Examination arrangement

Examination arrangement: School exam - multiple choice
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam - multiple choice 100/100 4 hours D

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

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024
Extraordinary deadline for course registration: 2024-06-01

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Computer and Information Science
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science

Timetable

List view
Calendar view

Examination

Examination arrangement: School exam - multiple choice

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD School exam - multiple choice 100/100 D INSPERA
Room Building Number of candidates
Summer UTS School exam - multiple choice 100/100 D INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU