DD2437 Artificial Neural Networks and Deep Architectures 7.5 credits
Information for research students about course offerings
PhD students should register for the corresponding third-cycle course. The main difference in the examination amounts to an additional requirement for conducting a project relevant to the course.
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Content and learning outcomes
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 by making extensive use of available data. The learning rule and network architecture determine specific computational properties of the ANN. The course offers a possibility to develop the conceptual and theoretical understanding of the computability of ANNs starting from simpler systems and then gradually study more advanced architectures. A wide range of learning types are thus studied – from strictly supervised to purely exploratory unsupervised situations. 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.
Intended learning outcomes
After completing the course, the students shall be able to
- describe the structure and the function of the most common artificial neural network types (ANN), e.g. (feedforward) multi layer perceptron, recurrent network, self organising maps, Boltzmann machine, deep belief network, autoencoder, and give examples of their applications
- explain mechanisms of supervised/unsupervised learning from data- and information processing in different ANN architectures, and give an account for derivations of the basic ANN algorithms discussed in the course
- demonstrate when and how deep architectures lead to increased performance in pattern recognition and data mining problems
- quantitatively analyse the process and outcomes of learning in ANNs, and account for their shortcomings, limitations
- apply, validate and evaluate suggested types of ANNs in typical small problems in the realm of regression, prediction, pattern recognition, scheduling and optimisation
- design and implement ANN approaches to selected problems in pattern recognition, system identification or predictive analytics using commonly available development tools, and critically examine their effectiveness
in order to
- obtain an understanding of the technical potential as well as advantages and limitations of today's learning, adaptive and self-organizing systems,
- acquire the ANN practitioner’s competence to apply and develop ANN based solutions to data analytics problems.
Literature and preparations
Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.
Knowledge in linear algebra, 6 higher education credits, equivalent completed course SF1624/SF1672/SF1684/SF1694/IX1303.
Knowledge in statistics and probability theory, 6 higher education credits, equivalent completed course SF1900/SF1912-SF1925/SF1935.
The mandatory courses in mathematics, numerical analysis and computer science for D, E, and F-students or the equivalent.
 Stephen Marsland. Machine Learning, an Algorithmic Perspective, 2009,CSC-Press.
 Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning., 2016, MIT press.
Additional recommended literature will be provided on the course webpage.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- KON1 - Partial exam, 1.5 credits, grading scale: P, F
- LAB2 - Laboratory assignments, 4.0 credits, grading scale: P, F
- TEN3 - Written exam, 2.0 credits, grading scale: 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.
- 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.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web DD2437
Main field of study
The previous written examination TEN2 (3.5 higher education credits) is replaced by the written examination TEN3 (2 higher education credits) and three written tests that are combined to form the component KON1 (1.5 higher education credits). During the academic year 2022/2023 examination can be carried out within the framework of earlier instances (with TEN2) or the new model with TEN3 and KON1 (which together can be treated as earlier component TEN2).
In this course, the EECS code of honor applies, see: