Machine Learning, incl. Deep Learning, with R

Upper Confidence Bound) You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test). We will understand the theory behind deep neural networks. We will understand and implement convolutional neural networks - the most powerful technique for image recognition. Description Did you ever wonder how machines "learn" - in this course you will find out. We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ... For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions.