Applied Machine learning
In the modern IT world, businesses often have access to large amounts of data collected from customer management systems, web services, customer interaction, etc. The data in itself does not bring value to the business; we must bring meaning to the data to create value. Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data.
This course will focus on applied machine learning, where we learn what algorithms and approaches to apply on different types of data.
Target group
This course is for experienced developers working in the industry.
Content
The course includes the following:
- Supervised learning, different types of data and data processing
- Algorithms for handling text documents
- Algorithms for handling data with numerical and categorical attributes
- Neural Networks
- Deep Learning for image recognition
Practical information
All materials will be available digitally and include reading materials, lecturer images, etc. The lectures are pre-recorded and you can watch them at your convenience. Linked to each lecture is a live opportunity (Mondays at 3-5 p.m.) when you can log in online and get help from the teacher with questions, problems that you have encountered etc, these opportunities are not mandatory. On these occasions, the week's tasks will be distributed (these will also be sent out via email).
There will be 2 on-site workshops focusing on interaction with teachers and participants to share real-world experiences and insights. The course will be given in a flexible manner to facilitate the combination of course work with your professional commitments. We recommend that you work on a project during the course that you can use in your daily work, with your own data, your own problems, etc.
The total effort to complete this course is generally around 80 hours.
Language of instruction: lectures and the physical meetings will be held in Swedish, but some material may be in English.
Entry requirements
The basic eligibility for this course is a Bachelor degree. Candidates with relevant work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at the Bachelor level.
Schedule
Spring 2024
18/3, kl. 9-12: Växjö, Course introduction (mandatory)
25/3, kl.15-16.30: Online, Data and Learning
8/4, kl.15-16.30: Online, Naive Bayes
15/4, kl.15-16.30: Online, Numerical regression
22/4, kl.15-16.30: Online, Decision support
29/4, kl.16-17.30: Online, Kernel methods and SVMs
6/5, kl. 9-12: Växjö, Practical cases presentation from the participants (mandatory)
13/5, kl.15-16.30: Online, Neural Networks
20/5, kl.15-16.30: Online, Deep Learning
27/5, kl.15-16.30: Online, Ensemble Learning
3/6, kl. 9-12: Växjö, Practical cases presentation from the participants (mandatory)
Autumn 2024
30/9, kl. 9-12: Växjö, Course introduction (mandatory)
7/10, kl.15-16.30: Online, Data and Learning
14/10, kl.15-16.30: Online, Naive Bayes
21/10, kl.15-16.30: Online, Numerical regression
28/10, kl.15-16.30: Online, Decision support
4/11, kl.16-17.30: Online, Kernel methods and SVMs
11/11, kl. 9-12: Växjö, Practical cases presentation from the participants (mandatory)
18/11, kl.15-16.30: Online, Neural Networks
25/11, kl.15-16.30: Online, Deep Learning
2/12, kl.15-16.30: Online, Ensemble Learning
9/12, kl. 9-12: Växjö, Practical cases presentation from the participants (mandatory)
This course is developed within the project ”Expertkompetens om sociala medier och webbteknologi för innovation och tillväxt” and funded by the Swedish Knowledge Foundation (KK-stiftelsen). The course is also offered within the project Smart industry phase 2.