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didattica:magistrale:ml:ay_1819:main [2019/09/11 16:58] marcop [Course Objectives] |
didattica:magistrale:ml:ay_1819:main [2020/09/17 16:55] (current) |
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| <WRAP box round 95% center> | <WRAP box round 95% center> | ||
| 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: | ||
| - | 1)distinguish the various machine learning paradigms; | ||
| - | 2)know the learning theory | ||
| - | 3)know classification algorithms, regression, clustering and dimension reduction; | ||
| - | APPLYING KNOWLEDGE AND UNDERSTANDING | + | * distinguish the various machine learning paradigms; |
| - | Moreover, the student must demonstrate that he is able to: | + | * know the learning theory |
| - | 1) apply the different machine learning paradigms | + | * know classification algorithms, regression, clustering and dimension reduction; |
| - | 2) implement the classification, regression, clustering and dimensionality reduction algorithms; | + | |
| - | 3) design and implement systems able to learn automatically from real data and situations; | + | |
| - | COMMUNICATION SKILLS | + | APPLYING KNOWLEDGE AND UNDERSTANDING\\ |
| + | After completing the course, the student must demonstrate that he is able to: | ||
| - | At the end of this training activity, the student will be able to express himself clearly and with | + | * apply the different machine learning paradigms |
| - | appropriate terms, using the English language, in the learning discussions as well as expose the | + | * implement the classification, regression, clustering and dimensionality reduction algorithms; |
| - | results of a research concerning technical aspects of machine learning. | + | * design and implement systems able to learn automatically from real data and situations; |
| + | |||
| + | |||
| + | 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. | ||
| 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: | ||
| - | 1) Finding and learning the innumerable algorithms and techniques that are presented in the field | + | |
| - | of machine learning | + | * Finding and learning the innumerable algorithms and techniques that are presented in the field of machine learning |
| - | 2) implement and use the new algorithms | + | * Implementing and using the new algorithms |
| + | |||
| </WRAP> | </WRAP> | ||
| Line 68: | Line 70: | ||
| <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 171: | 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> | ||
