Starts week 36, 2020
The
course focuses mainly on the applied aspects of machine learning with special
emphasis on neural networks and deep learning.
Initially, the course gives an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear seperability and its limitations in classification is discussed. Then, different activation functions and the sigmoid perceptron is studied to solve non-linear classification problems.
Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning is covered. Feed-forward neural networks and the back-propagation algorithm will be presented. The course will also cover recurrent neural networks.
Finally, deep learning is discussed with emphasis on the basic prenciples and different types of deep learning neural networks.
Initially, the course gives an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear seperability and its limitations in classification is discussed. Then, different activation functions and the sigmoid perceptron is studied to solve non-linear classification problems.
Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning is covered. Feed-forward neural networks and the back-propagation algorithm will be presented. The course will also cover recurrent neural networks.
Finally, deep learning is discussed with emphasis on the basic prenciples and different types of deep learning neural networks.
- Starts and ends:
- wk36, 2020 - wk45, 2020
- Study Rate:
- 50%
- Location:
- Borlänge
- Time of Day:
- Day
- Teaching form:
- Normal
- Language:
- English
- Other:
- P: Course only offered as part of programme.
- Entry Qualifications:
-
- 30 credits Second Cycle within the Mainfield of Microdata Analysis or equivalent knowledge
- Application Code:
- HDA-H34YC
- Main field of study:
Tuition Fee
- First Tuition Fee Installment:
- 16,875 SEK
- Total Tuition Fee:
- 16,875 SEK
Information on application and tuition fees: www.universityadmissions.se.
Literature List
Literature lists are published at the latest one month ahead of the course start date.
To Literature ListCan we help you?
Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88
Course room in Learn
In the learning platform Learn you can find more information about the course.
Visit the course roomCourse Coordinator
Starts week 36, 2020
The
course focuses mainly on the applied aspects of machine learning with special
emphasis on neural networks and deep learning.
Initially, the course gives an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear seperability and its limitations in classification is discussed. Then, different activation functions and the sigmoid perceptron is studied to solve non-linear classification problems.
Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning is covered. Feed-forward neural networks and the back-propagation algorithm will be presented. The course will also cover recurrent neural networks.
Finally, deep learning is discussed with emphasis on the basic prenciples and different types of deep learning neural networks.
Initially, the course gives an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear seperability and its limitations in classification is discussed. Then, different activation functions and the sigmoid perceptron is studied to solve non-linear classification problems.
Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning is covered. Feed-forward neural networks and the back-propagation algorithm will be presented. The course will also cover recurrent neural networks.
Finally, deep learning is discussed with emphasis on the basic prenciples and different types of deep learning neural networks.
- Starts and ends:
- wk36, 2020 - wk45, 2020
- Study Rate:
- 50%
- Location:
- Borlänge
- Time of Day:
- Day
- Teaching form:
- Normal
- Language:
- English
- Entry Qualifications:
-
- 30 credits Second Cycle within the Mainfield of Microdata Analysis or equivalent knowledge
- Application Code:
- HDA-H359B
- Main field of study:
Literature List
Literature lists are published at the latest one month ahead of the course start date.
To Literature ListCan we help you?
Ask us about studying at Dalarna
support@du.se
+46 23-77 88 88
Course room in Learn
In the learning platform Learn you can find more information about the course.
Visit the course roomCourse Coordinator