Erinevus lehekülje "Data Mining (ITI8730)" redaktsioonide vahel
| (ei näidata sama kasutaja 84 vahepealset redaktsiooni) | |||
| 1. rida: | 1. rida: | ||
| − | Fall | + | |
| + | <span style="color:red"> Information for perspective students:</span> | ||
| + | |||
| + | <span style="color:red"> Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span> | ||
| + | |||
| + | <span style="color:red"> The course is open to students with valid TalTech UniID! | ||
| + | The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula. | ||
| + | </span> | ||
| + | |||
| + | <span style="color:red"> | ||
| + | Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd. | ||
| + | </span> | ||
| + | |||
| + | <span style="color:red"> | ||
| + | Those planning to use their own computers please install "R" and "R-studio". | ||
| + | </span> | ||
| + | |||
| + | |||
| + | Fall 2024 | ||
ITI8730: Data Mining and network analysis | ITI8730: Data Mining and network analysis | ||
| 5. rida: | 23. rida: | ||
Old code for this course is IDN0110 | Old code for this course is IDN0110 | ||
| − | Taught by: Sven Nõmm | + | Taught by: Prof. Sven Nõmm |
| + | |||
| + | Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten. | ||
EAP: 6.0 | EAP: 6.0 | ||
| − | Lectures: Tuesdays | + | Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building) |
| − | Labs (practices): Thursdays | + | Labs (practices): Thursdays 14:00 - 15:30 ICT-121 |
| − | |||
| − | |||
| − | |||
| − | + | Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00) | |
Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!! | Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!! | ||
| 26. rida: | 43. rida: | ||
==Overview == | ==Overview == | ||
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining: | The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining: | ||
| + | * Classification | ||
* Clustering | * Clustering | ||
| − | |||
* Association pattern mining | * Association pattern mining | ||
* Outlier analysis | * Outlier analysis | ||
| 34. rida: | 51. rida: | ||
* Data types and Data Preparation | * Data types and Data Preparation | ||
* Similarity and Distances, Association Pattern Mining, | * Similarity and Distances, Association Pattern Mining, | ||
| − | * Cluster Analysis | + | * Classification, Cluster Analysis, Outlier analysis |
* Data streams, Text Data, Time Series, Discrete Sequences, | * Data streams, Text Data, Time Series, Discrete Sequences, | ||
| − | * | + | * Graph Data, Social Network Analysis |
==Evaluation== | ==Evaluation== | ||
| − | * | + | *2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test. |
*3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays. | *3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays. | ||
| − | *final exam (gives | + | *final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer. |
| − | Exam prerequisites: All | + | Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher). |
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA. | Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA. | ||
| + | Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment. | ||
| − | =Lectures = | + | =Lectures and Time line = |
| − | == | + | == 03.09.24 Distance function == |
| − | [[Media: | + | [[Media:Lecture_01_DM2024_Introduction_distance_functions.pdf |Slides]] |
| − | |||
| − | |||
| − | |||
| − | == | + | == 10.09.24 Classification I == |
| − | [[Media: | + | [[Media:Lecture_02_DM2024_Classification_I.pdf |Slides]] |
| − | |||
| − | |||
| − | == | + | == 17.09.24 Classification II == |
| − | [[Media: | + | [[Media:Lecture_03_Classification_II_DM_2024.pdf |Slides]] |
| − | == | + | == 24.09.24 == |
| − | + | [[Media:Lecture_04_DM2024_Regression_analysis_and_data_preparation.pdf |Slides]] | |
| − | == | + | == 01.10.24 Cluster analysis I== |
| − | + | [[Media:Lecture_05_DM2024_Cluster_analysis_I.pdf |Slides]] | |
| − | == | + | == 08.10.24 Association pattern mining == |
| − | [[Media: | + | [[Media:Lecture_06_DM2024_Association_Pattern_Mining.pdf |Slides]] |
| − | == | + | == 15.10.24 Clustering II == |
| − | [[Media: | + | [[Media:Lecture_07_DM_2024_Cluster_analysis_EM_algorithm.pdf |Slides]] |
| + | == 22.10.24 Anomaly and Outlier Analysis == | ||
| + | [[Media:Lecture_08_DM2024_Anomaly_and_Outlier_Analysis.pdf |Slides]] | ||
| − | |||
| − | |||
| − | == | + | == 05.11.24 Similarity and Distance II == |
| − | + | [[Media:Lecture_09_DM2024_Similarity_and_Distance_2.pdf |Slides]] | |
| − | |||
Viimane redaktsioon: 4. november 2024, kell 15:55
Information for perspective students:
Lecture schedule and slides content are tentative. Order of the topics has changed compared to the last year. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!
The course is open to students with valid TalTech UniID! The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula.
Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 2nd.
Those planning to use their own computers please install "R" and "R-studio".
Fall 2024
ITI8730: Data Mining and network analysis
Old code for this course is IDN0110
Taught by: Prof. Sven Nõmm
Teaching assistants: Mihhail Daniljuk, Anton Osvald Kuusk, Jaak Kapten.
EAP: 6.0
Lectures: Tuesdays 12:00 - 13:30 ICO-217 (IT college building)
Labs (practices): Thursdays 14:00 - 15:30 ICT-121
Link to join MS Teams (will be provided to the students regestrated via ÕIS on Monday September the 2nd by 17:00)
Consultation: by appointment only Please do not hesitate to ask for appointment!!! For communication please use the following e-mail: sven.nomm@taltech.ee
Prerequisites to join the course
Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language.
Overview
The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:
- Classification
- Clustering
- Association pattern mining
- Outlier analysis
Main topics of the course:
- Data types and Data Preparation
- Similarity and Distances, Association Pattern Mining,
- Classification, Cluster Analysis, Outlier analysis
- Data streams, Text Data, Time Series, Discrete Sequences,
- Graph Data, Social Network Analysis
Evaluation
- 2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.
- 3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.
- final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.
Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).
Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA. Please note below are the slides from previous year, ordering of some topic and some content has change. Use it for reference purposes only. Up to data slides are provided by means of TalTech Moodle Environment.
Lectures and Time line
03.09.24 Distance function
10.09.24 Classification I
17.09.24 Classification II
24.09.24
01.10.24 Cluster analysis I
08.10.24 Association pattern mining
15.10.24 Clustering II
22.10.24 Anomaly and Outlier Analysis