In this online study programme, you’ll explore the principles of mathematics and statistics that are crucial for developing AI. Computer science is the foundation of AI, so you’ll also be diving into topics like machine learning, deep and reinforcement learning.
From there, you’ll go on to focus on speech and image processing, cloud computing and software development. This gives you well-rounded knowledge to serve you in any future technical development position.
For the final part of your studies in applied artificial intelligence, before you begin your thesis, you decide what topic you want to focus on – choose from a wide variety of topics related to automation, production and management. Use this stage to really double down on the skills you want to develop according to to the career you want to have.
| Module | Courses | Total Time (weeks) | Hours per Day |
|---|---|---|---|
| Artificial Intelligence | Introduction to Artificial Intelligence | 10 weeks | 1-2 hours/day |
| Introduction to Academic Work | Study Skills for Academic Success | 2 weeks | 2-3 hours/day |
| Introduction to Programming with Python | Python for Everybody Specialization | 20 weeks | 1-2 hours/day |
| Mathematics: Analysis | Khan Academy - Calculus | 15 weeks | 1 hour/day |
| Collaborative Work | Teamwork Skills: Communicating Effectively in Groups | 4 weeks | 1-2 hours/day |
| Statistics - Probability and Descriptive Statistics | Introduction to Statistics | 10 weeks | 2-3 hours/day |
Recommended Books:
- "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig
- "Python Crash Course" by Eric Matthes
- "Introduction to Probability and Statistics for Engineers and Scientists" by Sheldon M. Ross
| Module | Courses | Total Time (weeks) | Hours per Day |
|---|---|---|---|
| Object-Oriented and Functional Programming with Python | Advanced Python Programming | 8 weeks | 3-4 hours/week |
| Mathematics: Linear Algebra | Khan Academy - Linear Algebra | 12 weeks | 1-2 hours/day |
| Intercultural and Ethical Decision-Making | Ethics in Technology | 6 weeks | 1-2 hours/week |
| Statistics - Inferential Statistics | Statistical Inference | 7 weeks | 3-4 hours/week |
| Cloud Computing | Cloud Computing Concepts | 16 weeks | 1-2 hours/day |
| Cloud Programming | Cloud Native Development | 12 weeks | 2-3 hours/day |
Recommended Books:
- "Python Programming: An Introduction to Computer Science" by John Zelle
- "Linear Algebra and Its Applications" by David C. Lay
- "Cloud Computing: Concepts, Technology & Architecture" by Thomas Erl
| Module | Courses | Total Time (weeks) | Hours per Day |
|---|---|---|---|
| Machine Learning - Supervised Learning | Machine Learning by Andrew Ng | 11 weeks | 5-7 hours/week |
| Machine Learning - Unsupervised Learning and Feature Engineering | Coursera: Machine Learning Specialization | 24 weeks | 5-7 hours/week |
| Neural Nets and Deep Learning | Deep Learning Specialization | 24 weeks | 5-6 hours/week |
| Introduction to Computer Vision | Computer Vision Basics | 8 weeks | 2-3 hours/day |
| Project: Computer Vision | Implementing Computer Vision Projects | 4 weeks | 2-3 hours/week |
| Introduction to Reinforcement Learning | Reinforcement Learning: An Introduction | 12 weeks | 5-6 hours/week |
Recommended Books:
- "Pattern Recognition and Machine Learning" by Christopher M. Bishop
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
| Module | Courses | Total Time (weeks) | Hours per Day |
|---|---|---|---|
| Introduction to NLP | Natural Language Processing with Python | 10 weeks | 5-7 hours/week |
| Project: NLP | NLP Projects | 12 weeks | 10-15 hours/week |
| Introduction to Data Protection and IT Security | Cybersecurity Essentials | 6 weeks | 2-4 hours/week |
| Data Science Software Engineering | Data Science Project Lifecycle | 5 weeks | 2-4 hours/week |
| Project: From Model to Production | Deploying Machine Learning Models | 6 weeks | 3-5 hours/week |
| Seminar: Ethical Considerations in Data Science | Ethics in Data Science | 4 weeks | 2-3 hours/week |
| User Experience | UX Design Fundamentals | 6 weeks | 3-5 hours/week |
| UX-Project OR Project: Edge AI | Advanced UX Design / Edge AI Projects | 20 weeks / 16 weeks | 4-5 hours/week / 4-5 hours/week |
| Introduction to Robotics | Robotics: Science and Systems | 16 weeks | 4-6 hours/week |
| Agile Project Management | Agile Methodology | 12 weeks | 4-5 hours/week |
Recommended Books and Resources:
- "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper
- "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking" by Foster Provost and Tom Fawcett
- "Robotics: Modelling, Planning and Control" by Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo
In your 5th semester, you'll need to choose an elective module. Here are some options with real courses and alternatives:
In your 6th semester, you'll need to choose an elective module. Here are some options with real courses and alternatives:
| Module | Courses | Total Time (weeks) | Hours per Day |
|---|---|---|---|
| International Marketing and Branding | International Marketing & Cross Industry Growth by Yonsei University (Coursera) | 4 weeks | 2-3 hours/week |
| Applied Sales | Sales Training: Techniques for a Human-Centric Sales Process (Udemy) | 4.5 hours | Varies |
| Supply Chain Management | Supply Chain Management Specialization by Rutgers University (Coursera) | 26 weeks | 3-5 hours/week |
| IT Project and Architecture Management | IT Project Management by Indian School of Business (Coursera) | 4 weeks | 2-4 hours/week |
| Psychology of Human Computer Interaction | Human-Computer Interaction by Georgia Tech (Coursera) | 10 weeks | 4-6 hours/week |
In your 6th semester, you'll need to choose an elective module. Here are some options with real courses and alternatives:
- Bachelor Thesis: A substantial project applying AI techniques to a real-world problem, supervised by faculty. Time and hours per day will vary based on project scope and student schedule.
This curriculum provides a comprehensive pathway through foundational AI concepts to advanced topics, integrating practical projects and ethical considerations. Adjust course links and resources according to your preferred learning platforms and availability.