With Applied Machine Learning you can teach systems to learn from data. Become attractive to employers and work with AI upon graduation.
Applied Machine Learning is an emerging technology discipline. As branch within artificial intelligence, machine learning focuses on teaching systems how to learn from data. The objective is to enable the system to identify patterns without further programming, making the discipline valuable for companies.
This one-year programme teaches you to make sense of the growing amounts of Big Data. It combines aspects of computer science, software development with Artificial Intelligence (AI) and data storage with data manipulation and visualization. Applied Machine Learning provides an increased level of automation which replaces time-consuming human activities through automatic techniques. This allows for improved accuracy and efficiency by discovering and exploiting any regularities in the data.
The programme focuses on practical skills in all aspects of Machine Learning, making you an attractive candidate for companies. The practical work will provide you with competence through professional workflows and pipelines used in the field of Applied Machine Learning.
The programme starts with Problem Based Learning. We then move on to Introduction to Programming, focusing on software design, algorithmic thinking and pseudo code. Next up is Introduction to Data Mining, with focus on pattern discovery, cluster analysis, classification and regression.
The next course is Data Pre-Processing, which touches on data standardization, feature engineering and feature selection for modelling. This course also teaches students to understand data quality issues and skills to link data pre-processing and data, as well as statistical techniques that allow you to prepare and format data in a structured way.
We then move on to Programming for Machine Learning where students learn skills to create code, format and manipulate data and program testing, as well as solving issues within your code. Next up is Computational Intelligence, focusing on machine learning algorithms for classification, clustering, and regression problems. This is where you learn to improve the accuracy of the machine learning models and optimize the application.
The last course is Data Visualisation, which teaches you the strengths and weaknesses of concepts and processes within big data visualisation, and tools and techniques for different data types. The year ends with an Exam Project, where you demonstrate the competence required during the academic year. The project challenges the candidate to find a real-world project to acquire practical experience in a professional setting.
Courses in Applied Machine Learning:
- Problem Based Learning
- Introduction to Programming
- Introduction to Data Mining
- Data Pre-Processing
- Programming for Machine Learning
- Computational Intelligence
- Data Visualisation
- Exam Project
After graduation the candidates possess the following learning outcomes:
- has knowledge of processes and methods that are used to solve data-driven problems
- has knowledge of processes tools that are used for programming with Python
- has knowledge of data collection and preparation that is used for Machine Learning tasks
- has knowledge of tools, development methodologies and processes that are used in Machine Learning applications
- can update his/her knowledge related to data mining, programming and machine learning
- has a knowledge of the IT industry and is familiar with the importance of Machine Learning
- understands the importance of effective and situation-appropriate data visualisations for communicating the outcome of Machine Learning
- can apply knowledge to identify and solve problems using Machine Learning
- masters descriptive statistical techniques and tools to evaluate and prepare data for Machine Learning modelling
- masters relevant tools and techniques for programming applications that utilize Machine Learning
- masters relevant tools, materials and techniques to solve real-world IT problems
- can find information relevant to developing Machine Learning applications
- can study a data problem situation and identify code and optimisation issues and what measures need to be implemented to solve the problem
- understands the ethical guidelines and codes of conduct that apply in Machine Learning
- can carry out Machine Learning projects using problem that can be solved using applied Machine Learning
- can build relations with his/her peers across discipline boundaries and with external target groups
- can develop Machine Learning applications using programming languages
- can develop work methods and present the results of Machine Learning applications
When purchasing a computer, make sure that it meets the following minimum requirements, as this will ensure that it has enough processing power, storage and memory to be able to load data sets and train machine learning models:
- CPU: Intel i5 or AMD Ryzen 5 or better
- RAM: 16GB
- Storage: 500GB (1TB recommended)
- Operating System:
- Windows 10 or MacOS 11 (Big Sur) or later.
- Ensure that you have full administrative privileges to install and manipulate all aspects of the system.
- Web camera and Microphone:
- Students are expected to interact with staff and peers via video and voice calls.
- A good quality headset (headphones + microphone) is recommended.
Once you start with the program, you will be informed of the specific software you will need to use for each course. In general, however, you should at least have access to the following:
- A spreadsheet editor, such as Microsoft Excel or Google Sheets.
- Word processing software, such as Microsoft Word or Google Docs.
There is a growing need for Machine Learning professionals across the world. Machine Learning can support decision making based on complex datasets. Companies have already begun to implement Machine Learning technology into their businesses in order to increase performance and efficiency thus reducing costs. Candidates are able to work in both national and international companies that require data scientists, software developers and AI engineers.