DD2437 Artificial Neural Networks and Deep Architectures 7.5 credits
(på svenska: Artificiella neuronnät och djupa arkitekturer 7,5 hp)

Language of instruction: English

Language of course memo: https://www.kth.se/student/kurser/kurs/DD2437?l=en

Course information: Course information, scope and learning outcomes

Course content

The course is concerned with computational problems in massively parallel artificial neural network (ANN) architectures, which rely on distributed simple computational nodes and robust learning algorithms that iteratively adjust the connections between the nodes heavily using the available data samples. The learning rule and network architecture determine specific computational properties of the ANN. The course offers an opportunity to develop the conceptual and theoretical understanding of computational capabilities of ANNs starting from simpler systems and progressively studying more advanced architectures, and hence exploring the breadth of learning types – from strictly supervised to purely explorative unsupervised mode. The course content therefore includes among others multi-layer perceptrons (MLPs), self-organising maps (SOMs), Boltzmann machines, Hopfield networks and state-of-the-art deep neural networks (DNNs) along with the corresponding learning algorithms. An important objective of the course is for the students to gain practical experience of selecting, developing, applying and validating suitable networks and algorithms to effectively address a broad class of regression, classification, temporal prediction, data modelling, explorative data analytics or clustering problems. Finally, the course provides revealing insights into the principles of generalisation capabilities of ANNs, which underlie their predictive power.

Learning outcomes

After completing the course the student should be able to

1. Describe the structure and function of the most common artificial neural network (ANN) types, e.g. multi-layer perceptron, recurrent network, self-organizing map, Boltzmann machine, deep belief network, autoencoder, and provide examples of their applications.
2. Explain mechanisms (algorithms) of supervised and unsupervised learning from data and information processing in different ANN architectures.
3. Quantitatively analyse the process and outcomes of learning in ANNs, and account for their shortcomings, limitations.
4. Solve typical small problems in the realm of regression, pattern recognition, scheduling and optimisation with the use of suggested types of ANNs.
5. (a) Design/devise and (b) implement and optimise ANN approaches to selected real-world problems in pattern recognition, system identification or predictive analytics using commonly available development tools, and (c) critically examine their applicability.

The purpose is to 

• obtain an understanding of the technical potential as well as advantages and limitations of adaptive, learning and self-organizing systems
• acquire the ANN practitioner’s competence to apply and develop ANN based solutions to data analytics problems.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

• LAB2 - Laboratory assignments, 4,0 hp, betygsskala: P, F
• TEN2 - Examination, 3,5 hp, betygsskala: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

A passed individual lab assignment can be credited in later course offerings if the assignment is unchanged (bonus points for other lab assignments will be discarded).

Opportunity to raise an approved grade via renewed examination

If the examination/re-examination is taken in later course offerings, all bonus points will be discarded.

Ethical approach

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex

Main field of study

Computer Science and Engineering, Information Technology