Please feel free to contact us if you have any questions

<aside> 💡 **Course Coordinators

Dr. Daniel Bos, MD, PhD: [email protected]
Dr. Kamran Ikram MD, PhD: [email protected] Dr. Gennady Roshchupkin, PhD: [email protected] Twitter Website**

</aside>

<aside> 💡 http://epi-server.erasmusmc.nl/rstudio/ - Server for practicals works only from Erasmus MC network

</aside>

Q&A

Further Reading:

Machine Learning:

1. "Pattern Recognition and Machine Learning" by Christopher M. Bishop
   • An excellent resource that covers a wide range of topics with clarity. It's more suitable for those with a solid foundation in linear algebra and probability.
2. "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
   • This book provides a deep dive into the theory behind many machine learning algorithms.
3. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy
   • A comprehensive text that covers both classic and modern topics in machine learning, all from a probabilistic perspective.
4. "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili
   • A hands-on guide to implementing machine learning algorithms in Python. Ideal for those who prefer a practical approach.

Deep Learning:

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
   • Often referred to as the "Deep Learning Bible", this book provides comprehensive coverage of the topic from three of the leading figures in the field.
2. "Neural Networks and Deep Learning: A Textbook" by Charu Aggarwal
   • Covers both the theory and practical application of neural networks and deep learning.
3. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
   • This is a very hands-on approach to learning both machine learning and deep learning using popular Python libraries.
4. "Dive into Deep Learning" by Aston Zhang, Zack C. Lipton, Mu Li, and Alexander J. Smola
   • An interactive deep learning book with code, math, and discussions. It's available online for free, but you can also get a printed version.
5. "Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani
   • Focuses specifically on using deep learning for computer vision tasks.
6. "Practical Deep Learning for Cloud, Mobile, and Edge" by Anirudh Koul, Siddha Ganju, and Meher Kasam

• This book is great for those looking to deploy deep learning models and apply them in real-world settings.

While some of these books assume prior knowledge of machine learning or deep learning concepts, others are suitable for beginners. Depending on your current knowledge and learning objectives, you can choose the most appropriate book(s) from this list.

Online Lectures:

VIDEOS

Practical Deep Learning for Coders 2022

Full Stack Deep Learning - Spring 2021

Neural Networks: Zero to Hero