AY 21/22 - Introduction to Machine Learning and Evolutionary Robotics

This page is about the courses named (actually the same course):

  • Introduction to Machine Learning and Evolutionary Robotics (332MI), for master program IN20, 9 CFUs
  • Apprendimento automatico ed estrazione dell’informazione dai dati (222MI), for master program IN19, 9 CFUs
  • Introduction to Machine Learning (470SM), for master programs SM35 and SM34, 6 CFUs
  • Machine Learning and Data Analytics (557EC), for master program EC71, 6 CFUs (part of a 9 CFUs course)

Program, goals, requirements #

Detailed program #

Part 1 (48h) #

  • Definition of Machine Learning; examples of applications of ML; taxonomy of ML problems; phases of design, development, and assessment of a ML system; terminology and mathematical notation.
  • Introduction to the software/language R; elements of data visualization.
  • Supervised learning.
    • Tree-based methods.
      • Decision and regression trees: learning and prediction; role of the parameters 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.
    • Naive-Bayes classification.
    • The K-nearest neighbors classifier.
  • Unsupervised learning.
    • Cluster analysis: hierarchical methods, partitional methods (k-means algorithm).
  • Text and natural language applications (text mining).
    • Sentiment analysis; features for text mining; common pre-processing steps.
    • Topic modeling.

Part 2 (24h) #

  • 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; properties of the representation.
    • Examples of common EAs: GA, GP, GE.
  • Evolutionary Robotics.
    • Brief foundations of Artificial Neural Networks and EC.
      • EA for neuroevolution.
    • Significant examples.
      • Evolution of Soft Robots morphologies (body).
      • Evolution of robotic agents controllers (brain).
      • Simultaneous evolution of body and brain.
    • Choosing the task, the fitness; reality gap.

Goal of the course #

Knowledge and understanding #

  • Know main kinds of problems which can be tackled with ML and EC and those ones concerning text and natural language
  • Know main ML 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.
  • Know design, development, and assessment phases of a EC-based system in the field of robotics.

Applying knowledge and understanding #

  • Formulate a formal problem statement for simple practical problems in order to tackle them with ML or EC/ER techniques.
  • Develop simple end-to-end ML systems.
  • Experimentally assess a simple end-to-end ML or EC/ER system.

Making judgements #

  • Judge the technical soundness of a ML or EC/ER system.
  • Judge the technical soundness of the assessment of a ML or EC/ER system.

Communication skills #

  • Describe, both in written and oral form, the motivations behind choices in the design, development, and assessment of a ML or EC/ER system, possibly exploiting simple plots.

Learning skills #

  • Retrieve information from scientific publications about ML or EC/ER techniques not explicitly presented in this course.

Requirements #

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.

Method, language, material #

Language of teaching #

English

Teaching method #

Frontal lectures with slide projection and live annotation; lab activities, under teacher supervision, in dealing with simple problems with ML techniques.

Course material #

Teacher slides and lab sketches #

All the material is available here:

  • Teacher slides, full pack for first part, full pack for second part.
  • Annotated slides; will be provided after the lectures.
  • Sketches for how to do the lab activities, in the form of R notebooks; please, fully enjoy the lab activity by not looking at these sketches too early.

The annotated slides, the unannotated slides, as well as the recordings of the lectures will be available also on the MS Teams team of the course.

Suggested textbooks #

  • 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. (for the 1st part of the course)
  • Kenneth A. De Jong. Evolutionary computation: a unified approach. MIT press, 2006. (for the 2nd part of the course)

Lectures timetable and course calendar #

The course will start on October, 4th. Lectures will be held in Classroom 2, 1st floor, C5 building, in Piazzale Europa campus.

The lectures will be given in person and I strongly recommend being in the room. In compliance with the current regulation students are required to book a place in the room. The lectures will also be cast in streaming using the MS Teams platform; the recordings of the lectures will be available on the MS Teams team of the course. Students can join the MS Teams team using this code: obyvj3a.

Notes about lecture dates #

The lecture of 29/10 is cancelled. Note that there are no lectures on 1/11 and 3/11, since of national and local holidays.

End-of-course test (exam) #

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. 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).

The oral exam of 20/1/2022 was accessible online for registered students who fullfil requirements to do exams online. UniTs credentials are required to access the call.

Final project #

The student chooses a problem among a closed, teacher-defined set of problems and proposes a solution based on ML 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 project assignment is here.

Written test #

Questions on theory and application with short open answers.