|Period:||period 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)|
|Participants:||up till now 65 subscriptions|
|Schedule:||Official schedule representation can be found in MyTimetable|
Information below may change slightly!
The impact of machine learning (ML) systems on our society has been increasing rapidly, ranging from systems that influence the content that we see online (e.g., ranking algorithms, advertising algorithms) to systems that enhance or even replace human decision making (e.g. in hiring processes). However, machine learning systems often perpetuate or even amplify societal biases—biases we are often not even aware of. What’s more, most machine learning systems are not transparent, which hampers their practical uptake and makes it challenging to know when to trust (or not trust) the output of these systems.
This course will familiarize students with a growing set of concepts and techniques to develop and assess machine learning systems, so that these systems are fair and interpretable, and can be used in responsible ways.
More specifically, we will discuss methods to measure the fairness of machine learning systems (ranging from fairness metrics to the creation of challenging evaluation datasets) and machine learning techniques to reduce biases in ML systems (e.g., data augmentation, representation learning).
The course will cover examples from various areas of AI. Given the expertise of the lecturers we will also zoom in on specific examples from natural language processing and multimodal affective computing research. Our discussion will also be informed by relevant literature from the social sciences. An interest in these areas is therefore desirable.
|Literature:||Most of the material we will read are academic articles. A few candidate readings are:
|Course form:||There will be lectures and practical exercises. Students are also expected to discuss and present academic articles. The course also contains a group project.|
|Exam form:||Group project, midterm, paper presentations, paper review|
|Minimum effort to qualify for 2nd chance exam:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|
|Description:||The course requires familiarity with machine learning (including neural networks) and proficiency in Python. It is recommended that students have completed at least one course on machine learning, such as “Pattern recognition” (INFOMPR) or “Advanced machine learning (INFOMAML)”. We expect students to already have experience with developing and evaluating machine learning systems. When in doubt, please contact the course coordinator (dr. Nguyen).|