Artificial neural networks

Elective course in master programs. Recommended for complex adaptive systems master.

Physics, Chalmers

Bachelor degree, inclusing at least 30 credits maths, plus programming.

This course describes how neural networks are used in machine learning. Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple processing elements that are connected in a network. Neural networks have revolutionised how we solve important problems in the engineering sciences, such as image analysis (object recognition and location), prediction, and control. The course gives an overview and a basic understanding of currently used neural-network algorithms, and exhibits similarities as well as differences between these methods. The main emphasis of this introductory course is on three connected topics: recurrent (Hopfield) networks, supervised learning with deep neural networks, and unsupervised learning (reinforcement learning). The goal is to explain how and why the algorithms work, when and how they fail, how to program the standard methods from scratch, and how to use packages that allow to easily set up and to efficiently run larger networks.

1. Statistical mechanics of neural nets
McCulloch-Pitts neurons, Hopfield nets, stochastic optimisation, Boltzmann machines
2. Deep learning
Perceptrons, backpropagation, stochastic gradient descent, deep learning, recurrent nets
3. Unsupervised learning
Hebbian learning, radial basis-function nets, reinforcement learning

-distinguish between supervised and unsupervised learning, explain the key principles of the corresponding algorithms, understand differences and similarities
-understand under which circumstances neural-net algorithms are the method of choice
-understand and explain strengths and weaknesses of the neural-net algorithms
-implement the algorithms introduced in class on a computer, both from scratch and using neural-net packages
-interpret the results of computer simulations and communicate conclusions in a clear, logical, and concise fashion
-understand the historical development of the field of machine learning with neural networks
-To have insights on ethical questions posed by machine learning, as well as possible risks, especially as related to gender and ethnicity
-To have an appreciation of the challenges and opportunities of working in an intercultural setting

The course is based on Machine learning with neural networkshttps://arxiv.org/abs/1901.05639.

Lectures

Homework problems
Programming with programming language of choice (commonly matlab or python). We use OpenTA for the homework problems.

Exercise classes
Homework problems and exam questions

Guest lectures
From research and/or industry, possibilities for MSc theses

Short instruction videos

The final grade is based on homework assignments (50%) as well as on a written examination (50%). To pass the course one must obtain points on both the exam and the homework assignments, of which a minimum number of points is required on the written exam.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.