Introduction to machine learning

Subject description

Prediction: classification and regression trees, logistic regression, naive Bayes classifier, LDA/QDA, nearest neighbors, evaluating goodness of fit. 

Unsupervised machine learning: clustering (hierarchical, k-Means).

Feature and model selection: cross-validation, bootstrap, filter methods, wrapper methods. 

Advanced prediction: regularization, generalized additive models, local regression. 

Combining models: bagging, boosting, random forests, ensemble learning. 

Support Vector Machines: for classification, for regression, optimization. 

Neural networks: learning neural networks, overfitting and other computational challenges. 

The subject is taught in programs

Objectives and competences

The methods covered in this course are fundamental to prediction, clustering and other quantitative data analysis tasks. Knowledge of these methods is key to applications of machine learning and understanding advanced machine learning methods. The course is also relevant to statisticians that do not specialize in machine learning, because it offers a set of new tools for data analysis. >

Teaching and learning methods

lectures, lab sessions, homework, consultations

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Knowledge and understanding: Understanding the basic concepts of machine learning.

Application: Classical machine learning methods are indispensable in modern data analysis and the foundation on which we can build a good understanding of advanced machine learning methods.

Reflection: Understanding of the theory on the basis of examples of application. Razumevanje povezav med strojnim učenjem in statistiko.

Transferable skills: Analytical ability. Ability to solve practical data analysis problems.

Basic sources and literature

  1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 6). New York: Springer.
  2. Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
  3. Géron A. (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Reilly Media, Inc.
  4. Witten I. H., Frank E., and Hall M. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd. ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

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