Exercises in Machine Learning

https://drive.google.com/file/d/17MAExAMp0DPeOmP3rug9K3Jgzg0_JV7y/view?usp=drive_link

We may have all heard the saying “use it or lose it”. We experience it when we feel rusty in a foreign language or sports that we have not practised in a while. Practice is important to maintain skills but it is also key when learning new ones. This is a reason why many textbooks and courses feature exercises. However, the solutions to the exercises feel often overly brief, or are sometimes not available at all. Rather than an opportunity to practice the new skills, the exercises then become a source of frustration and are ignored. This book contains a collection of exercises with detailed solutions. The level of detail is, hopefully, sufficient for the reader to follow the solutions and understand the techniques used. The exercises, however, are not a replacement of a textbook or course on machine learning. I assume that the reader has already seen the relevant theory and concepts and would now like to deepen their understanding through solving exercises. While coding and computer simulations are extremely important in machine learning, the exercises in the book can (mostly) be solved with pen and paper. The focus on penand-paper exercises reduced length and simplified the presentation. Moreover, it allows the reader to strengthen their mathematical skills. However, the exercises are ideally paired with computer exercises to further deepen the understanding. The exercises collected here are mostly a union of exercises that I developed for the courses “Unsupervised Machine Learning” at the University of Helsinki and “Probabilistic Modelling and Reasoning” at the University of Edinburgh. The exercises do not comprehensively cover all of machine learning but focus strongly on unsupervised methods, inference and learning. I am grateful to my students for providing feedback and asking questions. Both helped to improve the quality of the exercises and solutions. I am further grateful to both universities for providing the research and teaching environment. My hope is that the collection of exercises will grow with time. I intend to add new exercises in the future and welcome contributions from the community. Latex source code is available at https://github.com/michaelgutmann/ml-pen-and-paper-exercises. Please use GitHub’s issues to report mistakes or typos, and please get in touch if you would like to make larger contributions