Course structure
Day 1
- What is machine learning?
- Types of machine learning (classification, regression, ranking, reinforcement learning, clustering, anomaly detection, recommendation, optimization)
- Data preparation (train, test and validation data sets, imbalanced and noisy data)
- Classification model evaluation (accuracy, precision, recall, confusion matrix, ROC, AUC)
- Basic algorithms for classification (baseline models, Naïve Bayes Classifier, Logistic regression, Support Vector Machines, decision trees, ensemble models)
- Quick Scikit-Learn tutorial (how to load and transform data, training models, predicting values, model pipelines and evaluation)
- Practical classification task
- Basic algorithms for regression (analytical methods, gradient descent, SVR, regression trees)
Day 2
- Basic algorithms for clustering (K-means, hierarchical clustering)
- Practical clustering task
- Introduction to artificial neural networks (why they are so popular, what their advantages and disadvantages are, perceptron neural network)
- Most frequently used activation functions (Sigmoid, Linear, Tanh, Relu, Softmax)
- Multi-Layer neural networks (back propagation algorithm, stochastic gradient descent, convolution, pooling, regularizations)
- Quick tutorial to Keras (sequential models, optimizers, training, data workflow)
- Practical classification and regression tasks using neural networks
