This course aims at introducing advanced methodologies of machine learning, pertaining to learning decision processes, enhancement of effectiveness and combined usage of basic algorithms, and analysis and preparation of available data for their effective exploitation. The course’s learning outcome includes deep understanding of the performance of machine learning methodologies, the ability of combining them for solving demanding problems and the ability of data analysis and preprocessing for aligning the data effectively with an appropriate methodology.
Maximum Likelihood Classifiers.
Bayesian Concept Learning, Likelihood, Model fitting, Naive Bayes Classifier, Bayesian Networks.
k-Nearest Neighbor, Locally Weighted Regression, Case-Based Reasoning.
Introduction to Data Analysis with Python.
The libraries NumPy and Pandas. Introductory Examples. Data visualization with the matplotlib library.
Tree representation, Hypothesis Space Search, Information Gain, ID3 Algorithm, C4.5 Algorithm.
Ensemble Learning and Boosting.
Adaboost Algorithm, Random Trees, Combinations of Classifiers.
Python Programming for Machine Learning.
Programming classifiers with the library Scikit-learn.
Graphical and Programming Environment, Case Studies, Experimentation.
Linear Regression, Logistic Regression, Supervised Learning Workflow and Algorithms, Support Vector Machines, Unsupervised Learning, Applications
Multidimensional Data Processing.
R Language – Applications.
- T. Mitchell. Machine Learning. McGraw-Hill (International Edition), 1997.
- C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
- W. McKinney. Python for Data Analysis. O’Reilly, 2012.
- S. Raschka. Python Machine Learning. Packt Publishing, 2015.
- I. H. Witten, E. Frank, M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 3rd edition, 2011.