Neural Networks

This course can be taken as a single subject course.

However, the student is expected to have some basic knowledge of AL and ML, as well as experience in Python programming.

The course focuses on different approaches into the artificial intelligence domain focusing on neuralnet works.

The course introduces support vector machines (SVM) and compares the advantages of end-to-end training offered by Neural network models. It comprises of five modules:

• Multi-layer perceptron: Focusing on topics such as application / programming / understanding of concept of a perceptron, the multi-layer-perceptron,
• Convolution neural nets: Feed-forward neural nets, convolution neural nets (CNN) with emphasis placed on applications related to image analysis and related topics. VGG-Net, Res-NET.
• Recurrent neural nets: Recurrent Neural Networks (RNNs), Long Short-Term Memory networks(LSTMs), and Gated Recurrent Units (GRUs) for handling sequential data.
• Reinforcement learning: Basic introduction to reinforcement learning and deep reinforcement learning, emphasis will be placed on applications related to Q-learning for training (robotic /autonomous) agents and advance model architectures.
• Generative models: Introduction to Variational auto-encoders (VAEs) and Generative adversarial networks (GANs).

General study qualification with Mathematics R1+R2 and Physics FYS1. Application code: 9391

Recommended prerequisites:

• DTE-2602 Introduction to Machine Learning and Artificial Intelligence
• DTE-2510 Introduction to programming
• DTE-2511 Advanced programming
• Experience in Python programming

On completion of the course, the successful student is expected to have the following:

Knowledge

The student will have:

• An overview of history and numerous approaches within training deep neural networks.
• Understanding of "The curse of dimensionality" in AI.
• Basic understanding of back-propagation and complexity.

Skills

The student should be able to:

• Program, adapt and apply neural nets in different application domains.
• Identify and define features in a complex environment.
• Think critically with theoretical framework underpinning an deep learning architecture.

General Competence

• Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.

Anbefalte forkunnskaper

DTE-2510 Introduction to programming, DTE-2511 Advanced programming, DTE-2602 Introduction to Machine Learning and AI

The subject uses so-called "Flipped classroom", i.e., lectures are posted online continuously during the semester in the form of short instructional videos and demonstrations. In addition, exercises and control questions related to each video are used.

The subject teaches in the autumn semester with teacher-led and assistant-led learning and / or exercises.

Language of instruction and examination

Schedule

This course is open to incoming exchange students.

Study Level: Bachelor's

This course has admission prerequisites, which are listed under the Admission requirements section. Please review this information carefully before adding the course to your Learning Agreement.

The student is expected to have some basic knowledge of AL and ML, as well as experience in Python programming.

Recommended prerequisites:

• DTE-2602 Introduction to Machine Learning and Artificial Intelligence
• DTE-2510 Introduction to programming
• DTE-2511 Advanced programming
• Experience in Python programming

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.

More info about the coursework requirements

There are 4 mandatory exercises. These exercises can be submitted in either English or Norwegian.

Exercises submitted after the submission deadline will not be graded (PASS/FAIL).

All the 4 exercises must be passed to qualify for a grade in the course.

More info about the portfolio

The portfolio consists of programming Tasks:

• Two programming tasks.
• Each task can be submitted in English or Norwegian.
• Each task must be approved with a score of at least 30% to qualify for a final grade.

Re-sit examination

Info about the weighting of parts of the examination