IE500618 - Machine Learning


Examination arrangement

Examination arrangement: Oral examination
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Oral examination 100/100 E

Course content

This course assumes that you know close to nothing about Machine Learning (ML). Its goal is to give you the concept, the intuitions, and the tools you need to implement programs capable of learning from data. Throughout the course, we will cover several applied techniques, from the simple and the most common used (such as linear regression) to some of the Deep Learning techniques. In this course, you will learn how to implement your own toy versions of some algorithms and use actual frameworks in MATLAB and Python to work with benchmark problems and real-world datasets. Throughout the course, you will grow your own understanding of ML through concrete examples and a little bit of theory. In the classroom, you need to pick up your laptop, experiment with code examples provided in the book, and try your own code. Your participation, attendance, and ability to meet deadlines are crucially important to benefit from the course. The course contents are as follows, but not limited to:
[1] Introduction
[2] Supervised learning
[3] Unsupervised learning and preprocessing
[4] Representing data and engineering features
[5] Model evaluation and improvement
[6] Dimensionality reduction
[7] Artificial neural networks and gradient descent
[8] Convolutional neural networks
[9] Recurrent neural networks
[10] Training deep neural nets for images, video, text, and time-series

Learning outcome

Upon completion of the course, students will be expected to:
• Student will: lean basic concepts of machine learning such as supervised and unsupervised learning, data preprocessing, learning from data, different deep learning models, transfer learning and finally model selection and evaluation.
• Develop: students will learn how to develop a pipeline model that can be deployed to an endpoint using different machine learning platforms. Be able to design and implement various machine learning algorithms in a range of real-world applications. Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
• Utilize: student will learn how to utilize different machine learning platforms such as deep learning toolbox from MathWorks, and Python libraries such a keras, panda, tensrflow, etc.
• Understand key elements of how to use machine learning in applications that require images, videos, text, timeseries, etc. Have an understanding of the strengths and weaknesses of many popular machine learning approaches.
• Conduct research and apply tools and technologies in different areas such as HRI, image segmentation, sentiment analysis, text understanding and text summarization, object detection and tracking, tec.
• Gain an understanding of the strengths and weaknesses of many popular machine learning approaches and models. Have an understanding of the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
• Develop an awareness and understand of how to reliably use machine learning models in different areas.

Learning methods and activities

Lectures for 4 hours along with exercises covering the entire course. Other supporting material (video, text, etc.) may be used.

Mandatory assigments: 3 to be eligable for the oral exam

Compulsory assignments

  • Obligatoriske arbeidskrav

Further on evaluation

Oral exam

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Course materials

Reading list:
Recommended texts, but not limited to, for a reading list:
• A. Geron. Hands-On Machine Learning with SciKit-Learn & TensorFlow: concepts, tools, & techniques to build intelligent systems. 2017, O’Reilly.
• P. Harrington. Machine Learning in Action. 2012, Manning.
• J. Patterson and A. Gibson. Deep Learning: a practitioner’s approach. 2017, O’Reilly.
• N. Buduma. Fundamentals of Deep Learning: designing next generation machine intelligence algorithms. 2017, O’Reilly.
• D. Foste. Generative Deep Learning: teaching machines to paint, write, compose, and play. 2019, O’Reilly.

More on the course

Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  AUTUMN 2020

Language of instruction: English

Location: Ålesund

Subject area(s)
  • Computer Science
  • Engineering Subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of ICT and Natural Sciences



Examination arrangement: Oral examination

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Oral examination 100/100 E
Room Building Number of candidates
Spring UTS Oral examination 100/100 E
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.

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

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