Due to the Covid19 circumstances, for at least the summer 2020 exam sessions, the written test will be replaced by an oral interview. Interviews will be done on the MS Teams platform of UniTs or, in case of problems with the latter, with another similar tool (e.g., Google Meet). Precise instruction will be given to the students registered for each exam date after the registration deadline and before the exam. Students who want to listen to others students' interviews, but are not registered, should ask authorization to the teacher.
This page is about the courses named (actually the same course): Language of lecturesEnglish Detailed programPart 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.
 Treebased methods.
 Decision and regression trees: learning and prediction; role of the parameter and overfitting.
 Trees aggregation: bagging, Random Forest, boosting.
 Supervised learning system assessment: crossfold 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 preprocessing steps; topic modeling.
 Recommending systems.
 Contentbased filtering; collaborative filtering.
 Assessment metrics: precision, recall, accuracy@K, diversity, serendipity.
 Evolutionary Computation (EC).
 Highlevel working scheme of an Evolutionary Algorithm (EA); terminology.
 Generational model; selection criteria; exploration/exploitation tradeoff; genetic operators with examples; fitness function; multiobjective 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 222MI) Supervised learning 2.
 The Bayes classifier.
 The Knearest neighbors classifier.
 Unupervised learning.
 Dimensionality reduction methods: principal component analysis; biplot.
 Cluster analysis: hierarchical methods, partitional methods (kmeans algorithm).
Suggested textbooksKenneth 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. Goal of the courseKnowledge and understandingKnow 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 highlevel 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 understandingFormulate a formal problem statement for simple practical problems in order to tackle them with ML, DM, or EC techniques. Develop simple endtoend ML or DM systems. Experimentally assess a simple endtoend ML or DM system. Making judgementsJudge the technical soundness of a ML or DM system. Judge the technical soundness of the assessment of a ML or DM system. Communication skillsDescribe, 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. Learning skillsRetrieve information from scientific publications about ML, DM or EC techniques not explicitly presented in this course. RequirementsBasics 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. Teaching methodFrontal lessons with blackboard and slide projection; exercises, under teacher supervision, in dealing with simple problems with ML or DM techniques. ExamDue to the Covid19 circumstances, for at least the summer 2020 exam sessions, the written test will be replaced by an oral interview. Interviews will be done on the MS Teams platform of UniTs or, in case of problems with the latter, with another similar tool (e.g., Google Meet). Precise instruction will be given to the students registered for each exam date after the registration deadline and before the exam. Students who want to listen to others students' interviews, but are not registered, should ask authorization to the teacher.
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 <18. Grades >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, teacherdefined 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%). The details are available here.
Student must register for the exam session of their interest using the online sistem (esse3). Note that there are deadlines for registration (usually 1 week before the session date). Lessons timetable and course calendarThe course will start on October, 22th. Lessons will be held in Classroom 3B, H2bis building, in Piazzale Europa campus. Course materialTheacher slidesThe course material (slides) is attached at the bottom of this page.
The full pack of slides might be updated during the course. The annotated slides will be provided after the lectures and are available here. Lecture recordings2nd part, only for 222MI:  10/12/19: one, two
 11/12/19: one, two
 12/12/19: broken machinery, no recordings; sorry! However, mostly lab.
 17/12/19: one
 18/12/19: one, two
 19/12/19: seminar on reinforcement learning and reality gap (slides); then lab
Results of students' assessmentEC71 (10 answers) SM35 (33 answers)
Descrizione domande 

D1  Le conoscenze preliminari possedute sono risultate sufficienti per la comprensione degli argomenti trattati?  D2  Il carico di studio di questo insegnamento è proporzionato ai crediti assegnati?  D3  Il materiale didattico (indicato o fornito) è adeguato per lo studio della materia?  D4  Le modalità di esame sono state definite in modo chiaro?  D5  Gli orari di svolgimento dell’attività didattica sono rispettati?  D6  Il docente stimola / motiva l’interesse verso la disciplina?  D7  Il docente espone gli argomenti in modo chiaro?  D8  Le attività didattiche integrative (esercitazioni, laboratori, seminari, ecc.) risultano utili ai fini dell’apprendimento? (se non sono previste attività didattiche integrative, rispondete non previste)  D9  L’insegnamento è stato svolto in maniera coerente con quanto dichiarato sul sito web del corso di studio?  D10  Il personale docente è effettivamente reperibile per chiarimenti e spiegazioni?  D11  Sei interessato agli argomenti dell’insegnamento?  D12  Sei complessivamente soddisfatto dell’insegnamento? 

Updating...
Ċ Eric Medvet, Dec 17, 2019, 10:48 AM
Ċ Eric Medvet, Dec 2, 2019, 11:53 PM
