Course syllabus for Advanced machine learning with neural networks

Course syllabus adopted 2021-02-08 by Head of Programme (or corresponding).

Overview

  • Swedish nameAvancerad maskininlärning med neurala nätverk
  • CodeTIF360
  • Credits7.5 Credits
  • OwnerMPCAS
  • Education cycleSecond-cycle
  • Main field of studyEngineering Physics
  • DepartmentPHYSICS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 11111
  • Maximum participants80 (at least 10% of the seats are reserved for exchange students)
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0120 Project 7.5 c
Grading: TH		0 c0 c0 c7.5 c0 c0 c

In programmes

Examiner

  • Giovanni Volpe
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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Analysis in one variable and several variables, linear algebra, programming. First course in machine learning with neural networks (FFR135, SSY340 or similar).

Aim

This course introduces students to recent developments and state-of-the-art methods in machine learning using artificial neural networks. This advanced course builds on Machine learning with neural networks (FFR135) and provides an in-depth analysis of many of the concepts and algorithms that were briefly introduced in that course, with particular emphasis on applications in the natural and engineering sciences. The goal is to become familiar with several advanced machine-learning methods, and to code them efficiently in Python using current neural-network packages. An essential part of the course are projects in deep learning and reinforcement learning.

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding

-       Describe the different available neural network models with their advantages and disadvantages

-       Find relevant literature to keep up with this quickly advancing field

Skills and ability

-       Implement a broad range of state-of-the-art neural network models

-       Train and validate these models 

-       Optimize these models for a specific task 

-       Plan, manage and execute a small scale project in the field

-       Write a report of their results of the project

Judgement and approach

-       Critically analyse the advantages and disadvantages of the available neural network models

-       Benchmark the results of a neural network models against other models

-       Critically evaluate and discuss advances in the field of neural networks

Content

This course is project based and focus on state-of-the-art applications of neural networks which are of relevance to research and industry. 

 

Focus:

-       Which model should be employed for a given task?

-       How should models be benchmarked?

-       What are the tradeoffs between complexity, accuracy and risk of overtraining in practical settings?

-       How does one evaluate the quality of the predictions made by the model?

Organisation

-       1 initial class to give the students an overview of the course

-       3 homeworks to be done by each student with peer-review and followed by a lecture that explains the context of these homeworks

-       A series of lectures on current topics where machine learning is applied in cutting edge research and industry applications given by local and international experts

-       A group project

Examination including compulsory elements

The examination is based on

-       30% homeworks (10% for each)

-       20% final project presentation

-       50% final project report

A necessary (but not sufficient) requirement for passing grade is that 5/10 points are achieved in each homework.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.