Ma­chine Learn­ing and Deep Learn­ing

Course content

The course provides knowledge of various concepts, techniques and methods related to machine learning and deep learning. More specifically, it contains 

• Data pre-processing and exploratory data analysis 
• Principles of unsupervised and supervised machine learning 
• Unsupervised and supervised machine learning methods 
• Strengths and weaknesses of dimensionality reduction algorithms, principal component analysis
• Linear models for regression and classification 
• Neural Networks: feed-forward neural networks, backpropagation, convolutional neural networks
• Deep Learning: deep feed-forward networks, regularization for deep learning, optimization for training deep models, application of deep learning.
• Ethical issues in machine learning and deep learning.

Furthermore, the course provides the students with practical hands-on experience in machine learning using open-source libraries such as scikit-learn. After completing the course, the students will be able to apply and use various machine-learning techniques on real datasets.