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didattica:magistrale:ml:ay_1819:main [2019/09/11 17:01] marcop [Course Objectives] |
didattica:magistrale:ml:ay_1819:main [2020/09/17 16:55] (current) |
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| KNOWLEDGE AND UNDERSTANDING | KNOWLEDGE AND UNDERSTANDING | ||
| - | The aim of the course is to provide the student with knowledge and skills in the area of machine learning. | + | The aim of the course is to provide the student with knowledge and skills in the area of machine learning.\\ |
| At the end of the course the student should be able to: | At the end of the course the student should be able to: | ||
| Line 46: | Line 46: | ||
| - | APPLYING KNOWLEDGE AND UNDERSTANDING | + | APPLYING KNOWLEDGE AND UNDERSTANDING\\ |
| + | After completing the course, the student must demonstrate that he is able to: | ||
| - | Moreover, the student must demonstrate that he is able to: | ||
| - | | ||
| * apply the different machine learning paradigms | * apply the different machine learning paradigms | ||
| * implement the classification, regression, clustering and dimensionality reduction algorithms; | * implement the classification, regression, clustering and dimensionality reduction algorithms; | ||
| Line 55: | Line 54: | ||
| - | COMMUNICATION SKILLS | + | COMMUNICATION SKILLS\\ |
| - | + | At the end of this training activity, the student will be able to express himself clearly and with appropriate terms, using the English language, in the learning discussions as well as expose the results of a research concerning technical aspects of machine learning. | |
| - | At the end of this training activity, the student will be able to express himself clearly and with | + | |
| - | appropriate terms, using the English language, in the learning discussions as well as expose the | + | |
| - | results of a research concerning technical aspects of machine learning. | + | |
| LEARNING SKILLS | LEARNING SKILLS | ||
| + | At the end of this training activity the student will be able to: | ||
| - | At the end of this training activity the student will be able to: | + | * Finding and learning the innumerable algorithms and techniques that are presented in the field of machine learning |
| - | 1) Finding and learning the innumerable algorithms and techniques that are presented in the field of machine learning | + | * Implementing and using the new algorithms |
| - | 2) implement and use the new algorithms | + | |
| </WRAP> | </WRAP> | ||
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| <WRAP round 95% center box> | <WRAP round 95% center box> | ||
| - | * Learning theory and the "learning problem" | + | * Probabilistic learning theory and the "learning problem" |
| - | * The VC-dimension | + | * The VC-dimension (Proof of the maximum margin) |
| * Notions of Probability and Linear Algebra | * Notions of Probability and Linear Algebra | ||
| Line 176: | Line 173: | ||
| * Lesson4 27/06/2019 {{didattica:magistrale:ml:ay_1819:lezione4_phd_protetto.pdf|Lezione4}} | * Lesson4 27/06/2019 {{didattica:magistrale:ml:ay_1819:lezione4_phd_protetto.pdf|Lezione4}} | ||
| * Lesson5 04/07/2019 {{didattica:magistrale:ml:ay_1819:lezione5_phd_protetto.pdf|Lezione5}} | * Lesson5 04/07/2019 {{didattica:magistrale:ml:ay_1819:lezione5_phd_protetto.pdf|Lezione5}} | ||
| - | * Lesson6 13/07/2019 {{didattica:magistrale:ml:ay_1819:lezione6_phd_protetto.pdf|Lezione6}} | + | * Lesson6 13/07/2019 {{didattica:magistrale:ml:ay_1819:lezione6_protetto.pdf|Lezione6}} |
| </WRAP> | </WRAP> | ||
