Official course description:
Full info last published 15/05-20

Advanced Machine Learning

Course info
Language:
English
ECTS points:
15.0
Course code:
KSADMAL1KU
Participants max:
53
Offered to guest students:
no
Offered to exchange students:
-
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Computer Science
Staff
Course manager
Associate Professor
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
22 January 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

This is a complete 15 ECTS course on Machine Learning. Building on the math knowledge acquired from the course Linear Algebra and Probability, students will be introduced to Machine Learning during the first part of the course. In the second part, recent machine learning research will be addressed.

Description

The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets and convey the results.

Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; deep learning; reinforcement learning; kernel machines; clustering; graphical models; Bayesian estimation; ensemble methods, and statistical testing.

Formal prerequisites

Linear Algebra and Probability

Intended learning outcomes

After the course, the student should be able to:

  • Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
  • Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
  • Implement abstractly specified machine learning methods in an imperative programming language.
  • Combine and modify machine learning methods to analyse practical dataset and covey the results.
Learning activities

The first 10 weeks will consist of lectures and exercise sessions. The last 4 weeks will consist of project work where students will implement machine learning methods from a recent research article.

Mandatory activities

The mandatory activities are weekly exercises that the student solve prior to the mandatory exercise session and/or home and will present the solution to the exercise class if randomly picked by the TA. In order to be qualified for the exam, the student must have solved and volunteered to present his/her solution to 50% of the mandatory problems on average. The completion rate is computed from the lists where the student check, prior to the exercise session, those problem he/she has solved and will be ready to present to the class. On the basis of the presented solution, the TA will give feedback and discuss the solution with the class and complement the solution if necessary. The mandatory weekly exercises occur in the first 10 weeks of the course and facilitate continuous learning throughout the course. The second attempt will be provided for the students, who do not pass the mandatories in the first attempt, after the first ten weeks of the course.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

Ethem Alpaydin: Introduction to Machine Learning, third edition. The MIT Press. ISBN 978-0-262-02818-9

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 6%
  • Lectures: 18%
  • Exercises: 18%
  • Assignments: 18%
  • Project work, supervision included: 30%
  • Exam with preparation: 10%
Ordinary exam
Exam type:
D: Submission of written work with following oral, Internal (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.
Exam submisson description:
The project will take four weeks of the course where the students will work in groups to implement and evaluate a machine learning method from a recent research article.

The group makes their presentations together and afterwards the students participate In the dialogue individually while the rest of the group is outside the room.
Group submission:
Group
  • Group size: 2-4 students
Exam duration per student for the oral exam:
20 minutes
Group exam form:
Mixed 1



Time and date
Ordinary Exam - submission Mon, 14 Dec 2020, 08:00 - 14:00
Ordinary Exam Mon, 18 Jan 2021, 09:00 - 19:00
Ordinary Exam Tue, 19 Jan 2021, 09:00 - 19:00
Ordinary Exam Wed, 20 Jan 2021, 09:00 - 19:00