Knowledge and understanding.
Know main kinds of problems which can be tackled with ML, DM, and EC and those ones concerning text and natural language and recommendation
Know main ML and DM techniques; know the high-level working scheme of EAs.
Know design, development, and assessment phases of a ML system; know main assessment metrics and procedures suitable for a ML system.
Applying knowledge and understanding.
Formulate a formal problem statement for simple practical problems in order to tackle them with ML, DM, or EC techniques.
Develop simple end-to-end ML or DM systems.
Experimentally assess a simple end-to-end ML or DM system.
Judge the technical soundness of a ML or DM system.
Judge the technical soundness of the assessment of a ML or DM system.
Describe, both in written and oral form, the motivations behind choices in the design, development, and assessment of a ML or DM system, possibly exploiting simple plots.
Retrieve information from scientific publications about ML, DM or EC techniques not explicitly presented in this course.
Basics of statistics: basic graphical tools of data exploration; summary measures of variable distribution (mean, variance, quantiles); fundamentals of probability and of univariate and multivariate distribution of random variables; basics of linear regression analysis.
Basics of linear algebra: vectors, matrices, matrix operations; diagonalization and decomposition in singular values.
Basics of programming and data structures: algorithm, data types, loops, recursion, parallel execution, tree.
Part 1 (24h)
Definitions of Machine Learning and Data Mining; why ML and DM are hot topics; examples of applications of ML; phases of design, development, and assessment of a ML system; terminology.
Introduction to the software/language R; elements of data visualization.
Supervised learning 1.
Decision and regression trees: learning and prediction; role of the parameter and overfitting.
Trees aggregation: bagging, Random Forest, boosting.
Supervised learning system assessment: cross-fold validation; accuracy and other metrics; metrics for binary classification (FPR, FNR, EER, AUC) and ROC.
Support Vector Machines (SVM).
Separating hyperplane: maximal margin classifier; support vectors; learning as an optimization problem; maximal margin classifier limitations.
Soft margin classifier: learning, role of the parameter C.
Non linearly separable problems; kernel: brief background and main options (linear, polynomial, radial); intuition behind radial kernel; SVM,
Multiclass classification with SVM.
Part 2 (24 h)
Text and natural language applications (text mining)
Sentiment analysis; features for text mining; common pre-processing steps; topic modeling.
Content-based filtering; collaborative filtering.
Assessment metrics: precision, recall, accuracy@K, diversity, serendipity.
Evolutionary Computation (EC).
High-level working scheme of an Evolutionary Algorithm (EA); terminology.
Generational model; selection criteria; exploration/exploitation trade-off; genetic operators with examples; fitness function; multi-objective optimization and Pareto dominance; debugging of an evolutionary search; EA issues (diversity, variational inheritance, expressiveness); fitness landscape.
Examples of common EAs: GA, GP, GE.
Part 3 (24h) - only for 9CFU version
Supervised learning 2.
The Bayes classifier.
The K-nearest neighbors classifier.
Dimensionality reduction methods: principal component analysis; biplot.
Cluster analysis: hierarchical methods, partitional methods (k-means algorithm).
Frontal lessons with blackboard and slide projection; exercises, under teacher supervision, in dealing with simple problems with ML or DM techniques.
The exam consists of a project and a written test. The final grade is the average of the two grades: the exam is considered failed if at least one of the two grades is 25 are automatically registered; in the remaining cases, the student may repeat the exam.
Written test: questions on theory and application with short open answers.
Project (home assignment): the student chooses a problem among a closed, teacher-defined set of problems and proposes a solution based on ML, DM, or EC techniques. The expected outcome is a written document (few pages) including: the problem statement; one or more performance indexes able to capture any solution ability to solve the problem; a description of the proposed solution from the algorithmic point of view; the results and a discussion about the experimental assessment of the solution with, if applicable, information about used data. Student may form groups for the project: in this case, the document must show, for each student of the group, which activities the student took part in. The project is evaluated according to clarity (~ 50%), technical soundness (~ 33%), and results (~ 17%).
Kenneth A. De Jong. Evolutionary computation: a unified approach. MIT press, 2006
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning, with applications in R. Springer, Berlin: Springer Series in Statistics, 2014.