Course Syllabus

Artificial Neural Networks

and Deep Learning

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Semester & Location:

Spring 2022 - DIS Copenhagen

Type & Credits:

Core Course - 3 credits

Major Disciplines:

Computer Science, Information science, Mathematics

Prerequisite(s):

One year of computer science and one course in either probability theory, linear algebra, or statistics at university level. Knowledge of programming languages (e.g. in Python/Javascript/Java/C++/Matlab), algorithms, and data structures.

Faculty Members: Ulrich Mortensen, ulrich.mortensen@dis.dk
Program Director:

Natalia Landázuri Sáenz, natalia.landazuri@disstockholm.se

Time & Place:

Tuesday 8:30-11:25

Classroom: N7-C25 in Nørregaard 7

 

Course Description

Artificial Neural Networks are programs that write themselves when given an objective, some data, and abundant computing power. Recently, these programs have brought about a wide array of impressive innovations, such as self-driving cars, face recognition, and human-like speech generators.

This course offers you an introduction to Artificial Neural Networks and Deep Learning. With focus on both theory and practice, we cover models for various applications, how they are trained and validated, and how they can be deployed in the wild.

Classes will be a mix of short lectures and tutorials, hands-on problem solving, and project work in groups.

 

Faculty member

Ulrich Mortensen, PhD in Mechanics and Material Science from the Technical University of Denmark. PostDoc Fellow at DTU Wind Energy. Research in Composite Materials and Materials Mechanics with focus in multi-physics Numerical Modelling of Materials Manufacturing. MSc in Engineering Design and Applied Mechanics from the Technical University of Denmark 2016.

 

Prerequisites

One year of introduction to Computer Science and an introduction to probability theory, linear algebra or statistics at university level. Practical programming experience is required (e.g. in Python/Javascript/Java/C++/Matlab) and prior knowledge of algorithms and data structures is very useful.

 

Learning objectives

Upon successfully completing the course, the student will be able to: 

  • Understand how neural networks fit into the more general framework of machine learning, and what their limitations and advantages are in this context.
  • Write a neural network from scratch using Python,
  • Use the backpropagation algorithm to calculate weight gradients in a feed forward neural network by hand
  • Create neural networks with the Keras/Tensorflow package for Python.
  • Training and testing of the networks on a given dataset, and evaluating the performance of the networks.
  • Understand the motivation for different neural network architectures and select the appropriate  architecture for a given problem


Course elements

  • Python programming
  • Supervised machine learning
  • Logistic regression and Neural Network (NN) fundamentals
  • Gradient descent and backpropagation
  • Regularization, Cost functions and the vanishing/exploding gradient problem
  • Classification (feed forward NNs)
  • Image Classification (Convolutional NNs)
  • Sequence modelling (recurrent NNs)
  • Introduction to Auto Encoders, Variational Auto Encoders and Generative Adversarial Networks (GANs)

 

Course overview

  • Intro to machine learning and neural networks: supervised learning, logistic regression for classification, basic neural network structure, simple examples and motivation for deep networks.
  • Neural networks: forward propagation, cost functions, error backpropagation, training by gradient descent, bias/variance and under/overfitting, regularization.
  • 1 week travel (core course week).
  • Convolutional Neural Networks.
  • Recurrent Neural Networks.
  • Autoencoders and adversarial networks.
  • Final project

 

Readings

Most of the learning will be based on parts of the following books:

  • Goodfellow et al., Deep Learning.
  • Nielsen, Neural Networks and Deep Learning

Additional possible sources include blog posts, videos available online, and scientific papers.

 

Expectations of the Students

Students are expected to reach the preparation goal leading up to each session. Students who have little or no experience coding in Python should either follow a Python tutorial before the course starts, or prepare to invest some hours getting up to speed with the language once we start. Students should have a working laptop computer. It is advised that each machine has a least 4 GB of RAM and a reasonable processor (if it’s bought after 2012 you should be fine). The Unix operating system is preferred (OSX and Linux), but not a necessity.

 

Approach to Teaching

The course is designed around the principle of constructive alignment. The two major components in the course—the assignments and the final project—implement this principle by stating clear outcome goals of every activity and the course as a whole.

Assignments: Leading up to each session, students are given a "preparation goal" and a suggested list of materials they can use to reach it. Sessions start with a short lecture (less than 1 hour) that introduces the topic of the day, and then students work through a set of technical exercises. The students are required to hand in two assignments throughout the course (40% of their final grade, 20% each), which are composed of problems from the exercises they have solved in class. This gives the student a clear outcome goal for each session: "show up prepared and complete the exercises". It gives incentive to prepare and work focussed.

Final project: From the beginning of the course the students are aware that an outcome of the course is a project that, if done well, can add value to their professional portfolio. The project is a small study on some popular topic of their own choosing that they can investigate with data they have scraped or downloaded from the Internet. They submit the project in two parts: First, each team must compose a project proposal presentation which demonstrates that they have made a plan for their project and are able to hypothesize about the outcomes. Second, after they have completed their project they must communicate the results in the popular format of a blog post. The proposal video is a fun exercise that serves as a platform for sharing ideas between groups (we view them all in class) but it also forces them to start with a very comprehensive idea of the outcome in mind.

 

Assignments and Evaluation

During the course you will hand in two assignments containing selected exercises solved in class. Furthermore, you will complete a larger project that uses tools which have been taught in the class. An acceptable project will cover e.g. data scraping and analysis. You will be allowed to define your own project, but you can also get assistance from the teacher.

During the programming projects, you are allowed to consult freely with any of the other students and the instructor. Contributions from other students, however, must be acknowledged with citations in your final report, as required by academic standards. Contributions to your presentations must similarly be acknowledged. Needless to say, the right to consult does not include the right to copy — programs, papers, and presentations must be your own original work. 

The participation grade reflects a student's contributions to classes, exercises, comments on other students' questions on the Discussion boards, attendance and engagement with guest speakers and during field studies.  Inappropriate and/or unprofessional behaviour (e.g., sleeping during presentations, being rude towards our hosts during field studies) results in a score of 0 for participation for the entire semester.

When assigning the final grades, your efforts will weigh as follows: 

  • Participation: 20% (includes class/exercise/project behavior that is beneficial to the learning of others)
  • Mandatory assignments: 40% (two hand-ins, each counting for 20%)
  • Final project: 40% (10% proposal presentation, 30% project report and presentation)

 

Academic Regulations  

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on: 

Course Summary:

Date Details Due