Course Syllabus

Artificial Neural Networks and Deep Learning

Course Description

Artificial Neural Networks are programs that write themselves when given an objective as well as enough data and 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.

Course elements

• Python programming
• Supervised machine learning
• Logistic regression and neural network fundamentals
• Gradient descent and backpropagation
• Regularization and the vanishing gradient problem
• Classification (feed forward NNs)
• Image recognition (convolutional NNs)
• Sequence modelling (recurrent NNs)
• Manipulating data (auto encoders and adversarial NNs)

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

Learning Objectives

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

• Use the backpropagation algorithm to calculate weight gradients in a feed forward neural network by hand
• Understand the motivation for different neural network architectures and select the appropriate  architecture for a given problem
• Write a neural network from scratch in using Tensorflow in Python, train it untill convergence and test its performance given a dataset.
• Understand how neural networks fit into the more general framework of machine learning, and what their limitations and advantages are in this context.

Faculty

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.

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.

Both project and assignments are group efforts. The teacher will rate all the assignments, but you will also participate using the peer evaluation system Peergrade.io, where each handing is double-blind peer-reviewed by 3-4 students which, together with the teacher’s evaluation composes indicators towards the final grade. This creates more and fairer feedback for each group as well as evaluation that is less sensitive to mistakes. Students’ overall feedback quality is taken into account during grade evaluation.

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 behavior (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.

Grading

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

Academic Regulations  

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

• Course Enrollment and Grading  
• Academic Expectations and Honor Code 

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