Official course description:
Full info last published 27/06-22

Advanced Machine Learning for Data Science

Course info
Language:
English
ECTS points:
7.5
Course code:
KSAMLDS1KU
Participants max:
40
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
Programme
Level:
MSc. Master
Programme:
MSc in Data Science
Staff
Course manager
Assistant Professor
Teacher
Associate Professor
Course semester
Semester
Forår 2022
Start
31 January 2022
End
31 August 2022
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

In this course, we teach both advanced machine learning (ML) approaches and hands-on skills for applying these approaches to data science problems.

Description

We teach the current state of the art of machine learning (ML) and applying ML to interesting data science problems. The module builds on top of the BSc course on Introductions to Machine Learning. On this basis we cover both, theoretical and computational ML learning concepts and key mechanisms, as well as technical details for programming ML approaches hands-on in leading ML frameworks.

In particular, this course covers:

  • A recap on fundamental learning types, preprocessing, data repreentations and linear models,
  • learning theory, and deterministic vs. probabilistic machine learning,
  • supervised learning, including deep neural networks, generative & discriminative learning, and non-parametric learning such as in kernel methods 
  • unsupervised learning, including dimensionality reduction, clustering, and autoencoders,
  • sequence learning including recurrent neural networks, and attention
  • hybrid learning, including geometric learning, reinforcement learning, adaptive learning, semi-supervised learning,
  • regularization, optimization.

Application areas will include data mining, bioinformatics, natural language processing, computer vision, and robotic control.

In the accompanying tutorials, we will exercise to:

  • reproduce key approaches in ML frameworks such as Pytorch and Tensorflow,
  • monitor and analyse training and representation of ML approaches,
  • collaboratively develop & visualise ML approaches,
  • develop a complete ML solution on a task of choice in a small group


Formal prerequisites

Having courses on Applied Statistics and Linear Algebra and Optimisation completed is required. A good background in basic Machine Learning and object-oriented interpreted programming languages such as Python is recommended.

Intended learning outcomes

After the course, the student should be able to:

  • define and describe the state of the art approaches in machine learning (ML)
  • characterise and analyse key mechanisms in major ML approaches in-depth
  • implement novel ML mechanisms in leading ML frameworks and develop applications for large-scale data science problems.
Learning activities

The course will consist of lectures, accompanying exercises, and self-study material to deepen knowledge and follow personal interests with literature recommendations. In the exercises during the first ten weeks, the students will practise concepts, methods, and implementations based on prepared assignments in class. In the final four weeks, students will pair in small groups to solve a prepared mini-project of choice to practice a full machine learning cycle in a collaborative setting.

The oral examination will assess the students’ learning outcomes based on the provided material through literature and lecture as well as the practised assignments and mini-project.

Course literature

We focus mostly on the following books (available online for free) and will add specific papers during the lectures:


Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 20%
  • Lectures: 18%
  • Exercises: 24%
  • Project work, supervision included: 26%
  • Exam with preparation: 12%
Ordinary exam
Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.
Exam duration per student for the oral exam:
30 minutes


reexam
Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.
Exam duration per student for the oral exam:
30 minutes

Time and date
Ordinary Exam Wed, 15 Jun 2022, 09:00 - 20:55
Ordinary Exam Thu, 16 Jun 2022, 09:00 - 20:55
Ordinary Exam Fri, 17 Jun 2022, 09:00 - 20:55
Reexam Thu, 11 Aug 2022, 09:00 - 20:55