Subject description - MI-IKM
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Instructions
| MI-IKM | Internet and Classification Methods | ||
|---|---|---|---|
| Roles: | Extent of teaching: | 1P+1C | |
| Department: | 18105 | Language of teaching: | CS |
| Guarantors: | Completion: | Z,ZK | |
| Lecturers: | Credits: | 4 | |
| Tutors: | Semester: | L | |
Web page:
https://courses.fit.cvut.cz/MI-IKM/Anotation:
In this course, the students get acquainted with classification methods used in four important internet, or generally network applications: in spam filtering, in recommendation systems, in malware detection systems and in intrusion detection systems. However, they will learn more than only how classification is performed when solving these four kinds of problems. On the background of these applications, they get an overview of the fundamentals of classification methods. The course is taught in a 2-weeks cycle with 2-hour lectures and 2-hour exercises. During the exercises, the students on the one hand implement simple examples to topics from the lectures, on the other hand consult their semester tasks.Content:
In this course, the students get acquainted with classification methods used in four important internet, or generally network applications: in spam filtering, in recommendation systems, in malware detection systems and in intrusion detection systems. However, they will learn more than only how classification is performed when solving these four kinds of problems. On the background of these applications, they get an overview of the fundamentals of classification methods. The course is taught in a 2-weeks cycle with 2-hour lectures and 2-hour exercises. During the exercises, the students on the one hand implement simple examples to topics from the lectures, on the other hand consult their semester tasks.Course outlines:
Lecture 1. Three important internet applications of classification methods Lecture 2. Basic concepts concerning classification Lecture 3. Main types of classification methods. Lecture 4. When does a classifier make the least number of mistakes on new data? Lecture 5. When is the classification comprehensible for a user? Lecture 6. A team is superior to an individual.Exercises outline:
| 1. | Getting familiar with the system Matlab for those who haven't used it yet. | |
| 2. | Presentation of possible semester tasks on which the students will work at home for the assessment + simple examples for Lecture 2. | |
| 3. | Simple examples for Lectures 3.-6. + consulting the semester tasks. |
Literature:
Requirements:
Bez zvláštní požadavků. Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| Page updated 24.4.2024 17:51:15, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |