Deep Learning (E-DAT303)
The concepts covered in this course provide relevant theoretical and hands-on programming knowledge.
Course description for study year 2024-2025. Please note that changes may occur.
Facts
Course code
E-DAT303
Version
1
Credits (ECTS)
0
Semester tution start
Spring, Autumn
Number of semesters
1
Language of instruction
English
Content
- Introduction to Deep Learning (Deep Learning Fundamentals, AI vs. Machine Learning vs. Deep Learning - Relationship Overview)
- Artificial Neural Networks (Perceptrons, Intro to Artificial Neural Networks, Layers in Artificial Neural Networks, Activation Functions in Artificial Neural Networks, Loss Functions in Artificial Neural Networks, Training Artificial Neural Networks, Batch Size & Epochs in Artificial Neural Networks, Optimization Algorithms in Artificial Neural Networks, Learning Rates in Artificial Neural Networks, Backpropagation Intuition - Neural Network Training, Bias in Artificial Neural Networks)
- Additional Fundamental topics (Datasets for Deep Learning - Training, Validation, & Test Sets, Overfitting- Artificial Neural Networks, Underfitting- Artificial Neural Networks, Tensor flow, and Keras, Multi-Layer Perceptron (MLP), Classification with the Tensor flow and MLP, Regression with the Tensor flow and MLP)
- Convolutional Neural Networks (CNNs) (What are CNNs? Visualizing convolutional filters, Zero padding, Max pooling)
- Recurrent Neural Networks (RNN) (What are RNNs? Architecture, Applications of RNNs)
- LSTM (General structure of LSTM neural network, RNN neural network, and long-term dependence, LSTM neural network and long-term dependence, LSTM network architecture)
Learning outcome
After completing this course, you will:
- have a firm understanding of the fundamentals of modern neural networks and their practical use.
- Identify the deep learning algorithms that are more appropriate for various types of learning tasks in various domains.
- Implement deep learning algorithms and solve real-world problems
Required prerequisite knowledge
None
Recommended prerequisites
Introduction to Programming
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Home exam | 1/1 | 2 Hours | Passed / Not Passed |
Digital examNo graded exam, but multiple-choice questions.
Multiple-choice questions must be passed to get course certificate
Course teacher(s)
Course coordinator:
Mina FarmanbarHead of Department:
Tom RyenMethod of work
All the recorded lectures are published as videos at once and students have immediate access to the entire course content.
Open for
Enkeltemner TN - bachelornivå
Course assessment
There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.