Deep Learning

[0.50] credits · Undergraduate

This course covers both unsupervised learning and deep learning techniques. Clustering algorithms such as k-means clustering, density-based spatial clustering, hierarchical clustering and spectral clustering will be studied along with clustering evaluation metrics. Dimensionality reduction and anomaly detection will also be studied. Deep learning concepts including neural networks, activation functions, convolutional neural networks, recurrent neural networks, and the use of frameworks such as TensorFlow/Keras and PyTorch will be explored finishing with advanced deep learning techniques such as generative models and transfer learning.