Introduction to Machine Learning

This course is intended for beginners who have no or limited experience with machine learning and want to do their first steps in this field.

• basic knowledge of programing in Python
• high school level of mathematics

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, Naive 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
  

Previous courses

Python I - introduction to programming [PYTHON_INTRO]