# Introduction to Machine Learning

## Code

IT-MAL1

## Version

1.1

## Offered by

ICT Engineering

## ECTS

5### Prerequisites

The course requires mathematics corresponding to the admission requirements for the ICT Engineering Programme. Knowledge about statistical and probability theory similar to that offered in the course Stochastic Modelling and Processes is recommended. It is also recommended taking the elective course in Applied Linear Algebra simultaneously.

### Main purpose

• Unsupervised learning

• Voice recognition

• Neural Networks

### Knowledge

- predictive methods, e.g. regression and classification
- descriptive methods, e.g. clustering and factor analysis
- deep learning methods, e.g. neural networks.

### Skills

- Understand and apply a number of machine learning methods for knowledge detection in both un-structured and structured data examples
- Understand and compare the algorithms behind different data mining and machine learning methods
- Match and possibly combine methods for practical use in a reasonable context.

### Competences

- Make informed choices about the use of machine learning techniques
- Parametrisise machine learning algorithms for a given data material
- Design and develop a complete solution for a complex, realistic problem
- Communicate and discuss the solutions with professionals and non-specialists.

### Topics

### Teaching methods and study activities

**CATEGORY 1:**

Participation of lecturer and students - Initiated by the lecturer

- Lessons, scheduled
- Project guidance
- Exams and tests

**CATEGORY 2:**

65 hours or 50%

Participation of students - Initiated by the lecturer

- Assignments, self-study
- Project and group work
- Homework and preparation for exams
- Evaluation of the teaching

**CATEGORY 3:**

- Homework and preparation for exams
- Self-study
- Project work
- Study groups
- Literature search

### Resources

### Evaluation

### Examination

The course is evaluated based on two oral examinations.

The first examination is a group exam in which the students make a 10 minute presentation about their group assignment. This is followed by approx. 20 minutes of discussion between the students and the examiners. This discussion will evolve around two of the main topics of the course. The group examination takes a total of 30 minutes.

After the group exam, each student is then called for an individual 15 minute oral exam. This exam is a discussion about the two main topics that were not covered in the group examination. The student in not allowed to make a presentation at the individual oral exam. The 15 minutes include grading and feedback.

The student is given one grade based on both the group exam and the individual exam.

### Grading criteria

### Additional information

### Responsible

Richard Brooks

### Valid from

2/1/2020 12:00:00 AM

### Course type

6. semester

7. semester

Elective for the specialization Data Engineering

Electives

### Keywords

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