Advanced Machine Learning

Summary

The purpose of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise him or herself with recent developments in this field.

Admission requirements

  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level
  5. Passing grade in the course Statistical methods for Data Science
Participation in the course also requires knowledge obtained from the course Artificial intelligence for Data Science.

Syllabus

Syllabus for students spring 2021

Course Code:
DA633E revision 1
Swedish name:
Avancerad maskininlärning
Level of specialisation
A1N
Main fields of study:
Computer Science
Language:
English
Date of ratification:
25 March 2019
Decision-making body:
Faculty of Technology and Society
Enforcement date:
18 January 2021

Entry requirements

  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level
  5. Passing grade in the course Statistical methods for Data Science
Participation in the course also requires knowledge obtained from the course Artificial intelligence for Data Science.

Specialisation and progression relative to the degree regulations

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

Purpose

The purpose of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise him or herself with the current front line research within the field.

Contents

The course contains the following elements:

  • Data transformation, Data Augmentation, adjustment/calibration of model parameters (including Advanced Feature Extraction, Hyper-parameter Optimisation)
  • Interactive machine learning methods (including Human-Machine Collaboration, Active Learning, Online learning, Incremental Learning, Learning from Data Streams)
  • Meta-learning algorithms and Ensemble Methods
  • Advanced algorithms for supervised learning and unsupervised learning (with emphasis on discriminative and generative Deep Learning architectures)
  • Reinforcement learning (including Policy Search, Policy Iteration, Value Iteration, Q-learning)
  • Trends and current front line research in machine learning

Learning outcomes

Knowledge and understanding
For a passing grade the student shall be able to:

  • Explain advanced machine learning methods and how they are used in practice
Competence and abilities
For a pass grade the student shall be able to:
  • Implement advanced machine learning algorithms, both individually and in groups
  • Assimilate and use published research results in machine learning
  • Explore recent developments in commercial machine learning applications
  • Evaluate and compare the suitability of different methods for addressing a given problem
  • Interpret the relevance of machine learning results
Evaluation abilities and approach
For a pass grade the student shall be able to:
  • Analyse and evaluate academic publications in machine learning
  • Critically analyse strengths and weaknesses in scientific arguments for both theoretical and experimental results.

Learning activities

Lectures, data laboratories, seminars, project work (Kaggle competition)

Assessments

The students' achievements are assessed through a report on group projects (7 credits, UG), laboratory assignments (3 credits, UG) and written examination (5 credits, UA).
An A-E passing grade requires that all parts have been completed and passed. The final grade is based on the written examination

Grading system

Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U).

Course literature and other teaching materials

  • Aurlien Gron. 2017. Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st ed.). O'Reilly Media, Inc.
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
  • Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.
  • Witten, Ian H., Frank, Eibe & Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques (3rd ed.) Burlington, MA: Morgan Kaufmann.
• A collection of scientific articles will bed added to the above mentioned literature.

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

Interim rules

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

Other Information

The syllabus is a translation of a Swedish source text.

Contact

The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information

Application

29 March 2021 - 06 June 2021 Day-time 100% Malmö This course is offered as part of a program