PART A: Theory
(i) Supervised Learning: Support Vector Machines, Ensemble Methods, Hyper-parameters optimization, Handing Imbalanced Datasets. (ii) Time Series Using Regression Methods: Model trees, Neural Networks. (iii) Semi-Supervised Learning: Self-trained models, Active Learning. (iv) Text Classification, Image Classification, Sound Classification. (v) Deep Learning: Convolutional Neural Networks, Recurrent Networks. (vi) Reinforcement Learning.

PART B: Laboratory
Python for Data Science, Python libraries: scikit-learn, orange, imbalanced-learn, pandas, statsmodels, h2o, libact, nltk, scikit-image, SpeechRecognition, tensorflow, keras, keras-rl.