Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course EDA065F valid from Spring 2020

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  • The course aims to give an insight into the underlying mathematical and statistical principles for machine learning, as well as into the specific learning approach Reinforcement Learning.
  • The course is set up in three modules:
    In the first course module, we aim to ensure that all students master the basic mathematical tools (statistical framework, optimization, concentration) that constitute the foundations of the theory of Machine Learning.

    The second course module applies the tools introduced in the first module to recent solutions for supervised and unsupervised learning problems (SVM, Kernel methods, Deep learning, as well as clustering and cluster validation).

    The third course module contains an exhaustive introduction of theoretical and practical aspects of reinforcement learning (MDP, dynamic programming, Q-learning, policy-gradient, learning with function approximation, and recent Deep RL algorithms).
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • know the terms, concepts and mathematical / logical expressions used in the presented material,
    be able to understand and explain the presented concepts,
    be able to use the presented concepts to express a given problem with them
    know the conceptual and mathematical tools needed to solve given problems
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to solve given problems with the tools presented in the course, at least to a certain level of complexity
    decide upon the applicability of a certain tool in a given context outside the scope of the course
    transfer the learned concepts and tools to problems outside the scope of the course where applicable
Judgement and Approach
  • For a passing grade the doctoral student must
  • be able to judge the advantages, scope of applicability and limitations of the presented tools and concepts in a given (research related) context
    be able to evaluate the consequences of applying the tools, methods and concepts discussed in the course outside the course scope
Types of Instruction
  • Lectures
  • Laboratory exercises
  • Exercises
  • Self-study literature review
  • Miscellaneous
  • Each of the course modules is designed individually, containing some combination out of lectures, exercises, practical exercises, study material / reading advise, and homework assignments . A module consists of a two-day physical meeting with lectures and some form of exercises, as well as some homework assignments that the students can work on prior / after the meeting.
Examination Formats
  • Written assignments
  • Miscellaneous
  • The examination criteria aside the active participation in the physical meeting for each module are set by the teachers responsible for the respective module and communicated prior to or at the physical meeting for the module.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Specific assumed prior knowledge is communicated through the course website per module, which is available well in advance. Generally the course requires at least some knowledge in probabilistic representation and reasoning, graphical models, and first order logic. Students are expected to have a background in Computer Science, Mathematics, Engineering Physics, Electrical Engineering or a closely related topic.
Selection Criteria
  • Lecture material (slides, articles, and literature recommendations) and hand-in assignments are distributed via the course homepage or the used and announced course content management system respectively.
Further Information
Course code
  • EDA065F
Administrative Information
  • 2020-08-26
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2020‑01‑15 (approximate)

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