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🌠 Euron 3κΈ° μ‹¬ν™”ν•™μŠ΅νŒ€: DL, ML νŒ€ λ°œν‘œ 자료 정리 λ ˆν¬μ§€ν† λ¦¬

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2022-2-Euron-μ‹¬ν™”ν•™μŠ΅νŒ€

1️⃣ ML

Curriculum

μ£Όμ°¨ λ‚ μ§œ λ‚΄μš© λ°œν‘œμž λ°œν‘œ 자료
1 22/09/16 OT μ΅œν•˜κ²½ πŸ“š
2 22/09/23 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 1μž₯, 2μž₯ μ΅œν•˜κ²½, μ˜€μˆ˜μ§„, κΉ€μ˜ˆμ§„ πŸ“š
3 22/09/30 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 4μž₯ μ΄λ‹€ν˜„, κΉ€μ˜ˆμ§„, λ°•λ³΄μ˜ πŸ“š
4 22/10/07 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 5μž₯ λ°•μ§€μš΄, μ˜€μ—°μž¬, μ΅œν•˜κ²½ πŸ“š
5 22/10/14 쀑간고사 νœ΄μ‹κΈ°κ°„ - -
6 22/10/21 쀑간고사 νœ΄μ‹κΈ°κ°„ - -
7 22/10/28 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 6μž₯ μ΄μ„œμ˜, μ΄λ‹€ν˜„, μ†μ†Œν˜„ πŸ“š
8 22/11/04 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 7μž₯ μ˜€μˆ˜μ§„, κΉ€μ˜ˆμ§„, λ°•λ³΄μ˜ πŸ“š
9 22/11/11 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 8μž₯ λ°•μ§€μš΄, μ˜€μ—°μž¬, μ΄μ„œμ˜ πŸ“š
10 22/11/18 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 9μž₯ μ΅œν•˜κ²½, μ΄λ‹€ν˜„, μ†μ†Œν˜„ πŸ“š
11 22/11/25 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 10μž₯ μ˜€μˆ˜μ§„, κΉ€μ˜ˆμ§„, λ°•λ³΄μ˜ πŸ“š
12 22/12/02 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 12μž₯ λ°•μ§€μš΄, μ˜€μ—°μž¬, μ΄μ„œμ˜ πŸ“š
13 22/12/09 기말고사 νœ΄μ‹κΈ°κ°„ - -
14 22/12/16 기말고사 νœ΄μ‹κΈ°κ°„ - -
15 22/12/23 λ”₯λŸ¬λ‹ νŒŒμ΄ν† μΉ˜ κ΅κ³Όμ„œ 13μž₯ μ΅œν•˜κ²½, μ΄λ‹€ν˜„, μ†μ†Œν˜„ πŸ“š
16 22/12/30 μ•„μ΄λ°μ΄μ…˜ 전체 πŸ“š
17 23/01/06 ν”„λ‘œμ νŠΈ - -
18 23/01/13 ν”„λ‘œμ νŠΈ - -
19 23/01/20 ν”„λ‘œμ νŠΈ - -
20 23/01/27 ν”„λ‘œμ νŠΈ - -
21 23/02/03 ν”„λ‘œμ νŠΈ - -
22 23/02/10 ν”„λ‘œμ νŠΈ - -
23 23/02/17 ν”„λ‘œμ νŠΈ - -
24] 23/02/21 ν”„λ‘œμ νŠΈ - -

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2️⃣ DL

Curriculum

μ£Όμ°¨ λ‚ μ§œ λ‚΄μš© λ°œν‘œμž λ°œν‘œ 자료
1 22/09/16 OT μ΄λ‹€ν˜„ πŸ“š
2 22/09/23 1. Introduction; Machine Learning for Graphs μ΄λ‹€ν˜„, μ΅œν•˜κ²½ πŸ“š
3 22/09/30 2. Traditional Methods for ML on Graphs μ΅œμ§€μš°, μ΅œμ˜ˆμ€ πŸ“š
4 22/10/07 3. Node Embeddings κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š
5 22/10/14 쀑간고사 νœ΄μ‹κΈ°κ°„ - λ…Όλ¬Έ 리뷰 μ€€λΉ„ - πŸ“š
6 22/10/21 쀑간고사 νœ΄μ‹κΈ°κ°„ - λ…Όλ¬Έ 리뷰 μ€€λΉ„ - πŸ“š
7 22/10/28 Special Session β†’ λ…Όλ¬Έ 리뷰 ALL πŸ“š
8 22/11/04 4. Link Analysis: PageRank μ΄λ‹€ν˜„, μ΅œν•˜κ²½ πŸ“š
9 22/11/11 5. Label Propagation for Node Classification μ΅œμ§€μš°, μ΅œμ˜ˆμ€ πŸ“š
10 22/11/18 6. Graph Neural Networks 1: GNN Model κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š
11 22/11/25 7. Graph Neural Networks 2: Design Space μ΄λ‹€ν˜„, μ΅œν•˜κ²½ πŸ“š
12 22/12/02 8. Applications of Graph Neural Networks μ΅œμ§€μš°, μ΅œμ˜ˆμ€ πŸ“š
13 22/12/09 기말고사 νœ΄μ‹κΈ°κ°„ - λ…Όλ¬Έ 리뷰 μ€€λΉ„ - πŸ“š
14 22/12/16 기말고사 νœ΄μ‹κΈ°κ°„ - λ…Όλ¬Έ 리뷰 μ€€λΉ„ - πŸ“š
15 22/12/23 Special Session β†’ λ…Όλ¬Έ 리뷰 ALL πŸ“š
16 22/12/30 9. Theory of Graph Neural Networks κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š
17 23/01/06 10. Knowledge Graph Embeddings μ΄λ‹€ν˜„, μ΅œν•˜κ²½ πŸ“š
18 23/01/13 11. Reasoning over Knowledge Graphs μ΅œμ§€μš°, μ΅œμ˜ˆμ€ πŸ“š
19 23/01/20 12. Frequent Subgraph Mining with GNNs κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š
20 23/01/27 13. Community Structure in Networks μ΄λ‹€ν˜„, μ΅œν•˜κ²½ πŸ“š
21 23/02/03 14. Traditional Generative Models for Graphs μ΅œμ§€μš°, μ΅œμ˜ˆμ€ πŸ“š
22 23/02/10 15. Deep Generative Models for Graphs κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š
23 23/02/17 16. Advanced Topics on GNNs μ΄λ‹€ν˜„, μ΅œν•˜κ²½, μ΅œμ§€μš° πŸ“š
24] 23/02/21 17. Scaling Up GNNs μ΅œμ˜ˆμ€, κΉ€λ‚˜ν˜„, μ΄μ€λΉˆ πŸ“š

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🌠 Euron 3κΈ° μ‹¬ν™”ν•™μŠ΅νŒ€: DL, ML νŒ€ λ°œν‘œ 자료 정리 λ ˆν¬μ§€ν† λ¦¬

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