ACIT4015 Internet of Things Emneplan

In this course, the students will gain an advanced understanding of some of the most important principles of the Internet of Things (IoT). IoT enables the development of unique, innovative products and services. The students will become familiar with architecture and intelligent algorithms that govern IoT and pervasive computing. In addition, the students will get to investigate a particular IoT related case in more depth. The goal of the course is to give the students an overall understanding of IoT: from a technical point of view - and in relation to the consequences for society (for example privacy preservation, security) - when billions (or trillions) of units communicate with each other in ‘the cloud’.

General admission requirements for the program.

Computational Intelligence is concerned with modern, bio-inspired approaches to artificial intelligence (AI) and is an umbrella term for the fields of neural networks (NN), fuzzy systems (FS) and evolutionary computation (EC). This course offers a comprehensive and systematic introduction to the fundamental concepts, principles, and methods in the three fields, a part of machine learning and deep learning, and several advanced topics (neuro-fuzzy systems, neuro-evolution, or fuzzy clustering). The course will illustrate major CI concepts, principles and methods using various application examples in engineering, biomedicine and business. In addition, the overview, history, state-of-the-art, and future trends of AI and CI field will be covered. The main modules for lectures include:

• AI and CI: Overview and history
• Fundamentals of neural networks
• Introduction to deep learning
• Fuzzy sets, logic and systems
• Topics in evolutionary computation
• Advanced topics
• AI and CI: State-of-the-art and future

Students are expected to have the following learning outcomes in terms of knowledge, skills and general competence.

Knowledge

On successful completion of the course, the students have:

• an overview on different perspectives, history and future of AI and Computational Intelligence (CI) fields.
• familiarity with the essential terminologies, concepts, ideas, elements and principles in the three pillar fields of CI.
• an in-depth understanding of state-of-the-art CI methods (fuzzy systems, neural networks, evolutionary computation, deep learning, and hybrid AI techniques).
• knowledge and understanding of open problems and future challenges and opportunities in the AI and CI field.

Skills

On successful completion of the course, the students can: 

• determine when to use and deploy the CI methods learned for real-world applications.
• apply appropriate CI models and algorithms to address modeling and optimization problems in real-world applications.
• analyze complex and uncertain datasets with CI algorithms.

General competence

On successful completion of the course, the students can:

• program the CI models/algorithms.
• deploy CI systems/models in real-world applications.
• solve complex search, optimization or decision-making problems using evolutionary algorithms.

The course consists of lectures (theory), labs (practical exercises and computer simulations/experiments), group discussions, Q&As, as well as group projects. The group projects will be assigned from a list of the suggested topics/areas. The students will work in groups and finally submit the project report as well as the code. 

Practical exercises: Lab and Q&A sessions.

The following coursework is compulsory and must be approved before the student can take the exam:

Two compulsory assignments:

• One project proposal, outlining the rationale and plan for the project. Between 500 and 1000 words.
• One project report, documenting the project and its results. Between 2500 and 5000 words.

The deadlines for submitting the compulsory assignments and other details are stipulated in the teaching plan made available at the start of the semester.

All written material, but no communication will be allowed.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Two examiners. External examiner is used periodically.

This course will contain the following topics: 

• Sampling, reconstruction and aliasing 
• Impulse response and difference equations
• Fourier series and Fourier transform 
• Frequency analysis and frequency response
• Transfer functions, filter design of FIR and IIR filters 
• Image presentation and processing

The course has 8 ECTS of overlapping content toward ADSE1310.