Deep LearningLaajuus (5 cr)
Code: R504D80
Credits
5 op
Objective
The students knows the fundamental concepts of deep learning, including various neural networks for supervised and unsupervised learning. The student can use some popular deep learning libraries applied in real industry problems.
Content
- Linear and non-linear regression, classification
- Foundations of neural networks and deep learning
- Techniques to improve neural networks: regularization and optimizations, hyper-parameter tuning and deep learning frameworks
- Applying deep learning to real-world scenarios such as object recognition and computer vision, image and video processing, text analytics and natural language processing,
Assessment criteria, satisfactory (1)
The student is familiar with neural networks and related machine learning methods.
Assessment criteria, good (3)
The student is familiar with neural networks and related machine learning methods. The student knows how to apply common deep learning frameworks.
Assessment criteria, excellent (5)
The student can explain and apply their knowledge of neural networks and related machine learning methods. The student knows how to apply feasible deep learning frameworks for a variety of deep learning applications.
Enrollment
18.03.2024 - 31.07.2024
Timing
02.09.2024 - 30.11.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D22S
Objective
The students knows the fundamental concepts of deep learning, including various neural networks for supervised and unsupervised learning. The student can use some popular deep learning libraries applied in real industry problems.
Content
- Linear and non-linear regression, classification
- Foundations of neural networks and deep learning
- Techniques to improve neural networks: regularization and optimizations, hyper-parameter tuning and deep learning frameworks
- Applying deep learning to real-world scenarios such as object recognition and computer vision, image and video processing, text analytics and natural language processing,
Location and time
Lapland University of Applied Sciences, Rantavitikka Campus, 2.9.2024 - 13.12.2024
Materials
Lecture materials and exercises will be placed in the Moodle workspace.
Teaching methods
Lectures, workshops, examples, exercises and self-supervised work.
Exam schedules
The course will be graded based on personal work and exercise projects.
Content scheduling
- Foundations and theory of neural networks and deep learning
- Working on various types of data and use cases with various neural networks (ANN, MLP, CNN, RNN etc.)
- Techniques to improve neural networks: regularization and optimizations, hyper-parameter tuning and deep learning frameworks
+ other relevant topics
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student is familiar with neural networks and related machine learning methods.
Assessment criteria, good (3)
The student is familiar with neural networks and related machine learning methods. The student knows how to apply common deep learning frameworks.
Assessment criteria, excellent (5)
The student can explain and apply their knowledge of neural networks and related machine learning methods. The student knows how to apply feasible deep learning frameworks for a variety of deep learning applications.
Assessment methods and criteria
The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.
Enrollment
02.10.2023 - 04.02.2024
Timing
05.02.2024 - 24.05.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
- Finnish
Seats
0 - 30
Teachers
- Tuomas Valtanen
Responsible person
Tuomas Valtanen
Student groups
-
R54D21SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2021
Objective
The students knows the fundamental concepts of deep learning, including various neural networks for supervised and unsupervised learning. The student can use some popular deep learning libraries applied in real industry problems.
Content
- Linear and non-linear regression, classification
- Foundations of neural networks and deep learning
- Techniques to improve neural networks: regularization and optimizations, hyper-parameter tuning and deep learning frameworks
- Applying deep learning to real-world scenarios such as object recognition and computer vision, image and video processing, text analytics and natural language processing,
Location and time
Lapland University of Applied Sciences, Rantavitikka Campus, 8.1.2024 - 15.5.2024
Materials
Lecture materials and exercises will be placed in the Moodle workspace.
Teaching methods
Lectures, workshops, examples, exercises and self-supervised work.
Exam schedules
The course will be graded based on personal work and exercises.
Content scheduling
- Linear and non-linear regression, classification
- Foundations of neural networks and deep learning
- Working on various types of data and use cases with neural networks
- Techniques to improve neural networks: regularization and optimizations, hyper-parameter tuning and deep learning frameworks
+ other relevant topics
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student is familiar with neural networks and related machine learning methods.
Assessment criteria, good (3)
The student is familiar with neural networks and related machine learning methods. The student knows how to apply common deep learning frameworks.
Assessment criteria, excellent (5)
The student can explain and apply their knowledge of neural networks and related machine learning methods. The student knows how to apply feasible deep learning frameworks for a variety of deep learning applications.
Assessment methods and criteria
The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.