COURSE DETAIL
COURSE DETAIL
This course examines algorithms, tools, practices, and applications of machine learning. Topics include core methods such as supervised learning (classification and regression), unsupervised learning (clustering, principal component analysis), Bayesian estimation, neural networks; common practices in data pre-processing, hyper-parameter tuning, and model evaluation; tools/libraries/APIs such as scikit-learn, Theano/Keras, and multi/many-core CPU/GPU programming.
COURSE DETAIL
COURSE DETAIL
This course covers basic knowledge of computers, including networks, office software, web basics, and Word, Excel, and PowerPoint in Microsoft Office 2016 packages.
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
COURSE DETAIL
This course covers cognitive processes (such as observation and processing information, and using and storing it), emotions, and their interrelationship. The focus is on the role of these phenomena in the design and use of Information and Communication Technology. The course is relevant for students interested in human-computer interaction and (serious) games and training applications.
COURSE DETAIL
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