-Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. +Machine learning has great potential for improving products, processes and research. However, the width of the applications of machine learning techniques is narrowed by the lack of explanations of the methods used. The book by Molnar introduces the reader to interpretability of machine learning.
-After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME. In addition, the book presents methods specific to deep neural networks. +Within this seminar, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression; moreover, you have the change to get to learn about state-of-the-art literature and recent preogresses. The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions. In addition, the book presents methods specific to deep neural networks and evaluation of the interpretability of the methods.
All interpretation methods are explained in-depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
@@ -72,18 +72,21 @@ This seminar relies on the book Interpretable Machine Learning - A Guide for
Enrollment Procedure
-Students enrolled in this Pro-Seminar will send their favorite topics (possibly with priorities) to Simon Klüttermann until 07.10.2022. We will assign topics based on your choices until 14.10.2022. If you are uncertain about your choice, we will meet shortly before the deadline and answer your questions (probably in the first week of October). +Students enrolled in this Pro-Seminar will send their favorite topics (possibly with priorities) to Simon Klüttermann until 07.10.2022. We will assign topics based on your choices in the following week. We will organize a short meeting to answer your questions and present before the deadline.
-After you are assigned a topic, you will also be assigned a supervisor from us to help you with questions you might have. If you have general questions, you can also always write me (see Contacts below). -We will not provide a presentation course; hence you must take the one offered by the faculty. Also, we will hold the course in English. +You will also be assigned a supervisor who will help with the questions you might have on the specific topic. For general questions, you can also always write Simon Klüttermann. +We will not provide a presentation course; hence you must take the one offered by the faculty. Also, we will hold the course in English.
-We will distribute the Presentations over 1-3 days in the second half of January. Every Presentation should be between 25 and 30 minutes long. Also, you will have to hand in a written report about your topic before the deadline on 16.09.2022. Finally, you shall learn to address constructive criticism with your and the other presented research topics. To train this, we will assign to you two students' reports to critically comment until the end of the semester. You need to participate in every part of this seminar to pass it. +The final talks will be scheduled over 1-3 days in the second half of January (each talk: 25/ 30 minutes). A written report about your topic before the deadline. +
In the Pro-Seminar, you will learn how to work yourself into a topic, research related literature, and answer questions to this topic. To this end, you need to read the subchapters assigned to you and use different sources to verify and extend the statements made. Also, by listening and engaging with the other presentations, you will get a comprehensive understanding of interpretable machine learning methods. +You will learn to address constructive criticism on research topics. To train this, we will assign to you two students' reports to critically comment until the end of the semester. You need to participate in every part of this seminar to pass it. +