Teaching‎ > ‎

Machine Learning (Master of Robotics) 2018-2019

Program

  • 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.
    • Tree-based methods.
    • 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.

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.

Lessons timetable and course calendar

The course will start on January, 22nd.
Lessons will be held in the Master in Robotics reserved room, 1st floor, C5 building, in Piazzale Europa campus.

Lezioni

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.

Course material

The course material (slides) is attached at the bottom of this page.
The full pack of slides might be updated during the course.
Ċ
Eric Medvet,
Mar 4, 2019, 9:53 AM
ċ
testLearner.r
(1k)
Eric Medvet,
Mar 6, 2019, 1:47 AM