GRA 4150 Artificial Intelligence - Technologies and Applications
GRA 4150 Artificial Intelligence - Technologies and Applications
The huge amount of training data available together with the increase of computer power, has made it possible in the last two decades to witness an AI revolution. AI is in fact changing the world in front of us at a high speed as technology is growing fast in medicine, surveillance, virtual assistants and language translation, among others.
The main objectives of this course are to discuss how this AI revolution evolved historically, how AI methods are applied in businesses, and what are the potential benefits and harms of AI. The student will also get familiar with central AI methods, both in theory and in practice.
At the end of the course the student should know:
- central AI methods;
- how AI methods are employed in industries;
- ethical and practical consideration for the use of AI.
At the end of the course the student should be able to:
- program basic AI methods in Python;
- set up an AI algorithm and test its performance;
- analyze potential benefits and harms of AI.
At the end of the course the student should be able to:
- collaborate with AI specialists;
- understand AI literature;
- discuss opportunities, limitations, and future challenges in AI.
The course will cover the following topics:
- General introduction to AI, definitions and history;
- AI applications, in particular computer vision and natural language processing;
- Different supervised machine leaning-based methods;
- Training algorithms;
- Overfitting, underfitting, generalization, validation;
- Ethical concerns of AI in the society.
Teaching will consist of a 2/3rd synchronous part and of a 1/3rd asynchronous part. The synchronous portion will be a combination of lectures and guest lectures. In particular, lectures will involve slides, notes and Jupyter notebooks for the coding part. The asynchronous portion may involve pre-recorded digital lectures, reading material or exercises to be solved individually or in group.
Familiarity with Python is expected, in particular of the NumPy, Pandas and matplotlib libraries; some familiarity with object-oriented programming and with the scikit-learn library is also an advantage.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class. The platform itslearning will also be used for communications and for sharing lectures material.
Please note that students are responsible to ensure they have access to a computer, and are expected to have both Python and Jupyter notebook installed, as well as to update and maintain their Python libraries and dependencies as required for the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
- Linear regression analysis, at the level of GRA 4110 Applied Data Analytics
- Python, at the level of GRA 4142 Data Management and Python Programming
- Data modeling, at the level of GRA 4142 Data Management and Python Programming
| Assessments |
|---|
Exam category: Submission Form of assessment: Structured test Invigilation Weight: 100 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: . Exam code: GRA 41503 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 | The course will be a combination of lectures, tutorials, articles, programming exercises and guest lecturers. |
Student's own work with learning resources | 121 Hour(s) | There will be exercises to illustrate some of the topics in the course. |
Examination | 3 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.