Erinevus lehekülje "Data Mining (ITI8730)" redaktsioonide vahel
Mine navigeerimisribale
Mine otsikasti
| (ei näidata sama kasutaja 33 vahepealset redaktsiooni) | |||
| 1. rida: | 1. rida: | ||
| − | Information for perspective students: | + | <span style="color:red"> Information for perspective students:</span> |
| − | + | <span style="color:red"> Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!</span> | |
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| − | The course is open to students with valid TalTech UniID! | + | <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. | + | 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. |
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| + | Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 4th. | ||
| + | </span> | ||
| + | <span style="color:red"> | ||
| + | Those planning to use their own computers please install "R" and "R-studio". | ||
| + | </span> | ||
| − | + | Fall 2023 | |
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| − | Fall | ||
ITI8730: Data Mining and network analysis | ITI8730: Data Mining and network analysis | ||
| 23. rida: | 24. rida: | ||
Taught by: Sven Nõmm | Taught by: Sven Nõmm | ||
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| + | Teaching assistants Ilja Matjas, Rajesh Kalakoti | ||
EAP: 6.0 | EAP: 6.0 | ||
| − | Lectures: Tuesdays | + | Lectures: Tuesdays 12:15 - 13:45 ICT-A1 |
| − | Labs (practices): | + | Labs (practices): Thursdays 14:00 - 15:30 ICT-404 |
Link to join MS Teams | Link to join MS Teams | ||
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Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!! | Consultation: '''by appointment only''' Please do not hesitate to ask for appointment!!! | ||
| 51. rida: | 53. rida: | ||
* Cluster Analysis, Classification, Outlier analysis | * Cluster Analysis, Classification, 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 | + | *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 50 % of the final grade): Written report on assigned topic + discussion with lecturer. | *final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer. | ||
| 61. rida: | 63. rida: | ||
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. | ||
| − | =Lectures = | + | =Lectures and Time line = |
| − | == | + | == 05.09.23 Distance function == |
| − | [[Media: | + | [[Media:Lecture_01_DM2023_Introduction_distance_functions.pdf |Slides]] |
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| − | == | + | == 12.09.23 Cluster analysis I == |
| − | [[Media: | + | [[Media:Lecture_02_DM2023_Cluster_analysis_I.pdf |Slides]] |
| − | == | + | == 19.09.23 Cluster analysis II == |
| − | [[Media: | + | [[Media:Lecture_03_DM2023_Cluster_analysis_II.pdf |Slides]] |
| + | [[Media:Practice_03_DM_2023_Cluster_analysis_EM_algorithm.pdf |Slides (Practice)]] | ||
| − | == | + | == 26.09.23 Anomaly and outlier analysis == |
| − | [[Media: | + | [[Media:Lecture_04_DM2023_Anomaly_and_Outlier_Analysis.pdf |Slides]] |
| − | == | + | == 03.10.23 Classification I == |
| + | [[Media:Lecture_05_DM2023_Classification_I.pdf |Slides]] | ||
| − | == | + | == 10.10.23 Classification II == |
| − | [[Media: | + | [[Media:Lecture_06_Classification_II_DM_2023.pdf |Slides]] |
| + | == 17.10.23 Regression analysis == | ||
| + | [[Media:Lecture_07_DM2023_Regression_analysis_and_data_preparation.pdf |Slides]] | ||
| − | == | + | == 24.10.23 Association Pattern mining == |
| − | [[Media: | + | [[Media:Lecture_08_DM2023_Association_Pattern_Mining.pdf |Slides]] |
| − | == | + | == 31.10.23 Closed Book Test I == |
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| − | == | + | == 07.11.23 Distance and Similarity II == |
| − | [[Media: | + | [[Media:Lecture_09_DM2023_Similarity_and_Distance_2.pdf |Slides]] |
| − | == | + | == 14.11.23 Mining the Time series == |
| − | [[Media: | + | [[Media:Lecture_10_DM2023_Mining_Time_Series.pdf |Slides]] |
| − | [[Media: | + | == 21.11.23 Mining data streams == |
| + | [[Media:Lecture_11_DM2023_Mining_Data_Streams.pdf |Slides]] | ||
| − | + | == 28.11.23 Text data mining == | |
| + | [[Media:Lecture_12_DM2023_Text_Data_Mining.pdf |Slides]] | ||
| − | + | == 05.12.23 Graph data mining and Social analysis == | |
| + | [[Media:Lecture_13_DM2023_Mining_Data_Graph_Data.pdf |Slides]] | ||
| − | + | [[Media:Lecture_13_DM2023_Social_Network_analysis.pdf |Slides]] | |
| − | [[Media: | ||
| − | == | + | == 12.12.23 Privacy preserving data mining== |
| − | + | [[Media:Lecture_14_DM2023_Privacy_preserving_data_mining.pdf |Slides]] | |
| − | == | + | == 19.12.23 Closed Book Test II == |
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Viimane redaktsioon: 31. august 2023, kell 08:15
Information for perspective students:
Lecture schedule and slides content are tentative. 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 4th.
Those planning to use their own computers please install "R" and "R-studio".
Fall 2023
ITI8730: Data Mining and network analysis
Old code for this course is IDN0110
Taught by: Sven Nõmm
Teaching assistants Ilja Matjas, Rajesh Kalakoti
EAP: 6.0
Lectures: Tuesdays 12:15 - 13:45 ICT-A1
Labs (practices): Thursdays 14:00 - 15:30 ICT-404
Link to join MS Teams
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:
- Clustering
- Classification
- Association pattern mining
- Outlier analysis
Main topics of the course:
- Data types and Data Preparation
- Similarity and Distances, Association Pattern Mining,
- Cluster Analysis, Classification, 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.