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4. Anomaly Localization in multiple KPIs: case study1 FluxRank

_$68F44697-ADC7-4F9F-8A0A-1FC8FA2F2F42%&'N)*+-1PQVZ^]isLeftAligned_){1354.2001037597656, -340.49497985839844}4RSXpathType789:T_({0.992000, 0.714000, 0.051000, 1.000000}>?@WY789:X_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH\ _

5. Anomaly Localization in multiple KPIs: case study 2: ExplainIt

_$362D3B65-AFB1-48DF-8261-9CCAA675C6F9%&'`)*+-1bchlp]isLeftAligned_){703.20010375976562, -323.49497985839844}4deXpathType789:f_({0.149000, 0.733000, 0.820000, 1.000000} >?@ik789:j_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHn _

3.Anomaly Localization in multi-attribute KPIs: Case Study 2: Squeeze

_$9AE35699-1009-4839-B25B-428325BAFF17%&'r)*+-1tuz~]isLeftAligned_){698.20010375976562, -400.49497985839844}4vwXpathType789:x_({0.220000, 0.576000, 0.216000, 1.000000} >?@{}789:|_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. Anomaly Localization in multi-attribute KPIs: Case Study 1: HotSpot

_$AC7BD006-1AD5-4EFF-A633-041C1D0A92DC%&')*+-1]isLeftAligned_){740.20010375976562, -434.49497985839844}4XpathType789:_({0.863000, 0.133000, 0.157000, 1.000000} >?@789:_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Monte Carlo Tree Search

_$C6EC1CDD-5ECE-4629-B458-0F303AB18AA1_){1032.2001037597656, -386.49497985839844}4XpathType789#@_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

Week 10: KPI Anomaly Localization

 _$A8078571-4017-4CE0-9520-A039B94B02DB%&')*+-[layoutStyle_decreasingStrokeWidth%&')*+-1]isLeftAligned_){228.15504807691855, -365.20312499999329}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

3. Brief Introduction to assignments and projects

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2. Introduction to AIOps

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1. Introduction to the course

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Week1: Introduction

 _$BC37CA04-D4A3-45EE-8EBD-BEB89926C6DB%&')*+-MNRV[[layoutStyle_decreasingStrokeWidth);%&')*+-1]isLeftAligned_){1311.0955849678915, -202.17348094128988}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. Prefix

_$60D37C98-9AF3-4F57-B94C-BAC5130F473B%&')*+-1 ]isLeftAligned_){1311.0955849678915, -168.17348094128988}4  XpathType789: _({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

3. ATC 18

_$6F6298F9-06AE-4E51-8F7D-D5891172397B%&')*+-1 $(]isLeftAligned_){1311.0955849678915, -134.17348094128988}4XpathType789:_({0.149000, 0.733000, 0.820000, 1.000000}>?@!#789:"_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH& _

4. WWW19

_$048F451D-4D67-4BF1-9178-5D55F01B412A%&'*)*+-1,-26:]isLeftAligned_){715.74358496789148, -151.17348094128988}4./XpathType789:0_({0.992000, 0.714000, 0.051000, 1.000000} >?@35789:4_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH8 _

2. Transfer Learning

_$9ACFBB19-6AA9-440A-9B46-24B37AADC26C%&'<)*+-1>?DHL]isLeftAligned_){797.09558496789145, -185.17348094128988}4@AXpathType789:B_({0.863000, 0.133000, 0.157000, 1.000000} >?@EG789:F_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHJ _

1. KDD16

_$1D1D3284-8B85-4125-913F-45C7C656639F_){947.09558496789145, -184.67348094128988}4OPXpathType789Q_({0.102000, 0.333000, 0.412000, 1.000000}>?@SU789T_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHX _

Week 11:Event Prediction

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4. Introduction to Visualization

_$8C0C86B9-1EE0-45A5-ABAC-4EC647B35550%&'s)*+-1uv{]isLeftAligned_){250.48445796966553, -138.45142364501953}4wxXpathType789:y_){-0.029000, 0.429000, 0.771000, 1.000000}>?@|~789:}_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

5. Nengwen: Assignment 1

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3. Storytelling with Visualization

_$CDE9AFE4-C997-47FA-BFF2-F638B3E1D734%&')*+-1]isLeftAligned_*{-276.51554203033447, -155.45142364501953}4XpathType789:_({0.149000, 0.733000, 0.820000, 1.000000} >?@789:_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. ANM website

_$99643FF1-1EA2-4A46-8A85-09B7C8C80D18%&')*+-1]isLeftAligned_*{-371.51554203033447, -189.45142364501953}4XpathType789:_({0.071000, 0.529000, 0.871000, 1.000000} >?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Student Self-Introduction

_$157D8A5C-CAF6-47F2-9CF4-D7113E6695E1_*{-76.515542030334473, -173.95142364501953}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH o<p style='text-align: left; '><span style='color: rgba(22, 67, 85, 1.000000); font: 24px "Helvetica Neue"; -cocoa-font-postscriptname: "HelveticaNeue-Light"; '>Week2</span><span style='color: rgba(22, 67, 85, 1.000000); font: 24px "PingFang SC"; -cocoa-font-postscriptname: "PingFangSC-Light"; '></span><span style='color: rgba(22, 67, 85, 1.000000); font: 24px "Helvetica Neue"; -cocoa-font-postscriptname: "HelveticaNeue-Light"; '> Visualization</span></p> _$74E73EA1-9B86-4583-999A-A74DCDC1CEAE%&')*+-LMQUZ[layoutStyle_decreasingStrokeWidth(:%&')*+-1]isLeftAligned_){222.15134239196777, -44.925898551940918}4XpathType789:_){-0.029000, 0.429000, 0.771000, 1.000000}>?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

4. Information Gain

_$3027BCF4-8882-4057-9107-5E89567FA58F%&')*+-1]isLeftAligned_){222.15134239196777, -10.925898551940918}4XpathType789:_({0.573000, 0.165000, 0.647000, 1.000000}>?@789:_({0.573000, 0.165000, 0.647000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

5. Decision Trees

_$6D212CF7-5E37-4A20-8114-7ED383DB0ACF%&')*+-1]isLeftAligned_({222.15134239196777, 23.074101448059082}4XpathType789:_({0.992000, 0.714000, 0.051000, 1.000000}>?@789:_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

6. SIGCOMM 2013 Case Study

_$4A5E4EA6-C8CF-4838-90D3-33318EA89FEE%&')*+-1 ]isLeftAligned_({222.15134239196777, 57.074101448059082}4  XpathType789: _){-0.029000, 0.429000, 0.771000, 1.000000}>?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

7. Feature Selection

_$D4A68ABF-DA46-4089-B641-D89442D0D844%&')*+-1#']isLeftAligned_){-292.84865760803223, 40.074101448059082}4XpathType789:_({0.863000, 0.133000, 0.157000, 1.000000} >?@ "789:!_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH% _

3. Internet Basics

_$7A7B0A53-648F-42E0-9A62-FCC81924BEB7%&'))*+-1+,159]isLeftAligned_({-390.84865760803223, 6.074101448059082}4-.XpathType789:/_({0.220000, 0.576000, 0.216000, 1.000000} >?@24789:3_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH7 _

2. Correlation and Regression

_$DA2C3479-6DDA-4167-9BDC-486B163D510D%&';)*+-1=>CGK]isLeftAligned_*{-403.84865760803223, -27.925898551940918}4?@XpathType789:A_({0.049000, 0.633000, 0.720000, 1.000000} >?@DF789:E_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHI _

1. SIGCOMM 2011 Case Study

_$004A8931-AA9D-4D54-B624-39CC438BEAEC_*{-79.848657608032227, -10.425898551940918}4NOXpathType789P_({0.102000, 0.333000, 0.412000, 1.000000}>?@RT789S_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHW _

Week 3: Correlation

 _$3E49CA99-A90D-4AE6-94C5-39494EEE7616%&'\)*+]-^[layoutStyle_decreasingStrokeWidth_q%&'`)*+-1bchlp]isLeftAligned_&{1280.756567028986, 54.82110507245693}4deXpathType789:f_({0.071000, 0.529000, 0.871000, 1.000000}>?@ik789:j_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHn _

1. case study: 2019FSE

_$4302B599-263A-4C25-AF33-E66BFE23DAE9%&'r)*+-1tuz~]isLeftAligned_&{1280.756567028986, 88.82110507245693}4vwXpathType789:x_({0.149000, 0.733000, 0.820000, 1.000000}>?@{}789:|_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. case study: 2019 IEEE Cloud

_$52BA2CC2-D8BA-426D-A231-A6EAD210B141%&')*+-1]isLeftAligned_'{1280.756567028986, 122.82110507245693}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

3. case study: IEEE CC Grid

_$EE0B6FD7-3EC4-46DA-BB86-B6D99AA22202_'{909.75656702898596, 57.82110507245693}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

Week 12: Trace Anomaly Detection and Localization

 _$34C06FEE-2776-4184-938A-D5B66364D4FD%&')*+-1[layoutStyle_decreasingStrokeWidth_{-121.12890625, 107.1328125}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({1.000000, 1.000000, 1.000000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

No Class in Week 4&5

 _$6240052E-3CF3-484C-9D7A-652E37C28082%&')*+- [layoutStyle_decreasingStrokeWidth%&')*+-1]isLeftAligned_'{274.3564497837026, 210.62070141748961}4XpathType789:_({0.149000, 0.733000, 0.820000, 1.000000}>?@789:_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

3. RandomForest

_$46E59146-DA19-4159-8432-E08A4679055A%&')*+-1]isLeftAligned_'{274.3564497837026, 244.62070141748961}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

4. Case study: Opprentice

_$224C7255-1052-4675-8C81-43182310CD7E%&')*+-1]isLeftAligned_({-391.6435502162974, 233.62070141748961}4XpathType789:_({0.071000, 0.529000, 0.871000, 1.000000} >?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. Time series algorithms Tutorial;TSD;

_$602B08AC-130F-43AD-922F-5BB886012C78%&')*+-1]isLeftAligned_({-410.6435502162974, 199.62070141748961}4XpathType789:_({0.863000, 0.133000, 0.157000, 1.000000} >?@789:_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Accuracy_CV_Overfitting

_$6D13F464-9468-4912-8391-D5D79DF44DEA_({-113.6435502162974, 196.62070141748961}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@  789 _({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

Week 6: Time Series and its Anomaly Detection (I)

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1. CNN

_$E848ED9E-AEDF-4501-8C9E-A1FD7890E9AF%&'))*+-1+,159]isLeftAligned_'{1191.489130434783, 294.70946557970865}4-.XpathType789:/_({0.149000, 0.733000, 0.820000, 1.000000}>?@24789:3_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH7 _

2. case study: STAR

_$E6EEB1CB-E53C-40F7-B603-1572852C96C8%&';)*+-1=>CGK]isLeftAligned_'{1191.489130434783, 328.70946557970865}4?@XpathType789:A_({0.220000, 0.576000, 0.216000, 1.000000}>?@DF789:E_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHI _

3. case study: ICSE 2019

_$19612F91-1993-4D76-AFB6-40AC731D6D74%&'M)*+-1OPUY]]isLeftAligned_'{1191.489130434783, 362.70946557970865}4QRXpathType789:S_({0.863000, 0.133000, 0.157000, 1.000000}>?@VX789:W_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH[ _

4. case study: KDD 2015

_$A8EF26AE-B57D-4F57-AA33-75F1A3BDFDDB_({901.48913043478296, 280.70946557970865}4`aXpathType789b_({0.102000, 0.333000, 0.412000, 1.000000}>?@df789e_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHi _

Week 13. Incident Mangement

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3. Deep Learning

_$45E9F2D8-6571-4A86-A047-3B9462CCC41D%&')*+-1]isLeftAligned_({307.85031223297119, 392.80168151855469}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

4. Deep Generative Model

_$BE41B7C9-8912-4DE4-9EB6-87C5B612A933%&')*+-1]isLeftAligned_({307.85031223297119, 426.80168151855469}4XpathType789:_({0.863000, 0.133000, 0.157000, 1.000000}>?@789:_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

5. Case study: Donut

_$08FCC3D3-9FC9-4720-9EFD-016266C079D5%&')*+-1]isLeftAligned_){-427.14968776702881, 398.80168151855469}4XpathType789:_({0.573000, 0.165000, 0.647000, 1.000000} >?@789:_({0.573000, 0.165000, 0.647000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. Time-Series Anomaly Detection Service at Microsoft

_$1A01EFEB-99DA-40CD-ABD3-3B9F91153678%&')*+-]isLeftAligned%&')*+-1]isLeftAligned_){-442.14968776702881, 364.80168151855469}4XpathType789#@_({0.992000, 0.714000, 0.051000, 1.000000} >?@789:_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHYfontStyle9VitalicXfontSize]strikethroughXfontNameYalignmentYunderlineTbold#@2_({0.263000, 0.357000, 0.400000, 1.000000}]HelveticaNeue P_$28CF4EFB-F86B-4555-ADAC-3974285D2619_){-332.14968776702881, 364.80168151855469}4XpathType789:_({0.992000, 0.714000, 0.051000, 1.000000} >?@789:_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Project Introduction.

_$7B89D927-C304-4AB9-94A7-8D0844BE0380_){-76.149687767028809, 361.80168151855469}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFG#@r_

Week 7: Time Series Anomaly Detection (II)

 _$C408230B-0627-4B2D-9C53-F44A8B27C3B3%&')*+-bcgkp[layoutStyle_decreasingStrokeWidth%&')*+-MNSWa]isLeftAligned);%&')*+-1 ]isLeftAligned_'{1300.8155281836721, 453.5044233999422}4  XpathType789 _({0.071000, 0.529000, 0.871000, 1.000000}>?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Machine Translation

_$A32C39EE-38A6-4933-A492-521E9735C8F8%&')*+-1 $(]isLeftAligned_'{1300.8155281836721, 487.5044233999422}4XpathType789_({0.071000, 0.529000, 0.871000, 1.000000}>?@!#789:"_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH& _

2: case study: Kitnune

_$781857FF-C10B-42BE-A003-585987A208B6%&'*)*+-1,-26:]isLeftAligned_'{1300.8155281836721, 521.5044233999422}4./XpathType7890_({0.071000, 0.529000, 0.871000, 1.000000}>?@35789:4_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH8 _

3. Case study: 0day attack detection

_$B0585CD2-A0CD-4100-962E-6D7DBE53C915%&'<)*+-1>?DHL]isLeftAligned_'{1300.8155281836721, 577.5044233999422}4@AXpathType789B_({0.071000, 0.529000, 0.871000, 1.000000}>?@EG789:F_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHJ _

4. UEBA

_$42362779-0108-49D2-B70A-D8B42CF704D7_'{1190.8155281836721, 515.5044233999422}4OPXpathType789:Q_({0.071000, 0.529000, 0.871000, 1.000000}>?@TV789:U_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHX_9Z_({0.263000, 0.357000, 0.400000, 1.000000} P_$18C7A773-D51D-4E92-B8E5-1645703B70CE_'{903.81552818367209, 499.0044233999422}4deXpathType789f_({0.102000, 0.333000, 0.412000, 1.000000}>?@hj789i_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHm _

Week 14. Security

 _$8AC4797E-3101-421F-85A4-D40F979E0419%&'r)*+s-t[layoutStyle_decreasingStrokeWidthu%&'v)*+-1xy~]isLeftAligned_({280.02558612823486, 516.17697143554688}4z{XpathType789:|_({0.573000, 0.165000, 0.647000, 1.000000}>?@789:_({0.573000, 0.165000, 0.647000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

4. Case study: FT-Tree

_$662B4D1D-C1E1-41B2-A089-62862E30C3BB%&')*+-1]isLeftAligned_({280.02558612823486, 550.17697143554688}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFG#@t_

5. Case Study: LogParsing DSN 2016

_$24232E1B-AE58-4CE9-A641-953BB248D8DE%&')*+-1]isLeftAligned_({280.02558612823486, 584.17697143554688}4XpathType789:_({0.149000, 0.733000, 0.820000, 1.000000}>?@789:_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

6. Assignment 2 Introduction

_$C35536C8-08B6-4C10-9DDB-9D73D811CC35%&')*+-1]isLeftAligned_){-266.97441387176514, 584.17697143554688}4XpathType789:_){-0.029000, 0.429000, 0.771000, 1.000000} >?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

3. Clustering

_$7D4EE90D-25E5-4D12-B4F0-D9CBD15BE446%&')*+-1]isLeftAligned_){-253.97441387176514, 550.17697143554688}4XpathType789:_({0.573000, 0.165000, 0.647000, 1.000000} >?@789:_({0.573000, 0.165000, 0.647000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2.Similarity

_$40A29EBD-AE2F-4E07-88AD-C60E6651D0EA%&')*+-1]isLeftAligned_){-332.97441387176514, 516.17697143554688}4XpathType789:_({0.992000, 0.714000, 0.051000, 1.000000} >?@789:_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Learning from text

_$12A3E929-BD42-4943-BBAF-DB7973F9C136_){-92.974413871765137, 519.17697143554688}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

Week 8: Log Parsing and Anomaly Detection (I)

 _$8F6906E0-8A06-401B-8D86-5AB4AD68C6D0%&')*+-stx|[layoutStyle_decreasingStrokeWidth+=Oa%&')*+-1]isLeftAligned_({246.11988830566406, 697.48953247070312}4XpathType789:_({0.120000, 0.476000, 0.116000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

5. Deep Sequence Learning

_$0A391C4B-DE0F-4D73-90B0-04A6C44BEFAE%&')*+-1  ]isLeftAligned_({246.11988830566406, 731.48953247070312}4  XpathType789:_({0.049000, 0.633000, 0.720000, 1.000000}>?@789:_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

6. Case study: DeepLog

_$3C42DFC6-239D-433F-95F2-26B4220B2AA2%&')*+-1"&*]isLeftAligned_({246.11988830566406, 765.48953247070312}4XpathType789: _){-0.029000, 0.429000, 0.771000, 1.000000}>?@#%789:$_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH( _

7. Case study: LogAnomaly (Weibin)

_$9DB0B2D2-CA55-431D-B301-51F6741A6124%&',)*+-1./48<]isLeftAligned_){-364.88011169433594, 793.48953247070312}401XpathType789:2_({0.220000, 0.576000, 0.216000, 1.000000} >?@57789:6_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH: _

4.Logistic Regression

_$BFB30AF1-6966-41CE-8DFB-072E1B3EF53F%&'>)*+-1@AFJN]isLeftAligned_){-244.88011169433594, 759.48953247070312}4BCXpathType789:D_({0.149000, 0.733000, 0.820000, 1.000000} >?@GI789:H_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHL _

3.SVM

_$ED7BC03F-7487-4278-8DC3-3C67EF09E084%&'P)*+-1RSX\`]isLeftAligned_){-247.88011169433594, 725.48953247070312}4TUXpathType789:V_({0.071000, 0.529000, 0.871000, 1.000000} >?@Y[789:Z_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH^ _

2. PCA

_$A971042E-166A-4063-B74B-AB4F3BE9FF41%&'b)*+-1dejnr]isLeftAligned_){-426.88011169433594, 669.48953247070312}4fgXpathType789:h_){-0.029000, 0.429000, 0.771000, 1.000000} >?@km789:l_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGHp _

1. Case Study: Log Anomaly Detection (ISSRE 2016)

_$071AEFBA-EE33-4989-886E-C7F49F82CA04_){-119.88011169433594, 700.48953247070312}4uvXpathType789w_({0.102000, 0.333000, 0.412000, 1.000000}>?@y{789z_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH~ _

Week 9: Log Parsing and Anomaly Detection (II)

 _$E7B3E9B6-6961-4E72-98FE-55091705887C%&')*+-[layoutStyle_decreasingStrokeWidth%&')*+-1]isLeftAligned_'{1243.4576562803475, 663.8152036396051}4XpathType789:_({0.071000, 0.529000, 0.871000, 1.000000}>?@789:_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

1. Case study: Pensieve

_$B33A90C9-0456-4913-B2CE-379D65D5B20D%&')*+-1]isLeftAligned_'{1243.4576562803475, 697.8152036396051}4XpathType789:_({0.149000, 0.733000, 0.820000, 1.000000}>?@789:_({0.149000, 0.733000, 0.820000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

2. Deep Reinforcement Learning

_$A2BB27EA-5AE5-4613-80E4-F2699497D7D8%&')*+-1]isLeftAligned_'{1243.4576562803475, 731.8152036396051}4XpathType789:_({0.220000, 0.576000, 0.216000, 1.000000}>?@789:_({0.220000, 0.576000, 0.216000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFG _

3. case study: DeepRM

_$C956A523-8042-464A-8825-1E9BE3E8760C%&')*+-1]isLeftAligned_'{1243.4576562803475, 765.8152036396051}4XpathType789:_({0.863000, 0.133000, 0.157000, 1.000000}>?@789:_({0.863000, 0.133000, 0.157000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

4. Gradient Boosted Decision Trees

_$1BB4108E-F0BD-4DC4-A423-1A3356CB111F%&')*+-1]isLeftAligned_'{1243.4576562803475, 799.8152036396051}4XpathType789:_({0.992000, 0.714000, 0.051000, 1.000000}>?@789:_({0.992000, 0.714000, 0.051000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

5. Association Mining

_$4D80CD68-8874-4F2C-B9D2-4842C66E7C0B%&')*+-1]isLeftAligned_'{1243.4576562803475, 833.8152036396051}4XpathType789:_({0.573000, 0.165000, 0.647000, 1.000000}>?@789:_({0.573000, 0.165000, 0.647000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFG#@q_

6. Case study: Decima

_$BD5BE8C1-7F2E-489B-BBF5-1996741A015F_'{936.45765628034746, 717.8152036396051}4XpathType789_({0.102000, 0.333000, 0.412000, 1.000000}>?@789_({0.071000, 0.529000, 0.871000, 1.000000}_({1.000000, 1.000000, 1.000000, 1.000000}EFGH _

Week 15: Resource Management

 _$35AD2F57-5AA8-4E69-B503-488281AD9BF8_({0.906000, 0.922000, 0.933000, 1.000000}%-9ACEz  2DVabcdeghijsx#7OVa-.;EYcp'(T]fs9GHt}/<= 1 2 Y z  E p } ~ W X  % & 3 @ k PQx>ivw012Y~$OP]j(5`an{ -XYfs%2?jOPQx(1:Grs"/Z[hutu BCP][\)*7Do N O v !!!*!7!b!!!"5"6"]"""""""##C#P#Q$$$$*$O$[$s$~$$$$$$$%$%%%2%?%j%%%&V&W&~&&&&&&&'%'&'3'@'k'''(N(O(v((((((()))+)8)c))))*Y*Z********+(+)+6+C+n+++,G,H,o,,,,,,,---$-1-\---.C.D.k......./'/R/_/`2223#3H3T3l3{33333334"4#404=4h4445F5G5n5555555666"6/6Z66676777^777777788888I8t889.9/9V9w99999999: ::B:m:z:{;!;";I;j;x;y;;;;;;;< <5<`%>P>]>^? ??5?V?d?e????????@"@M@Z@[AAA/A\AeAnA{AAAABB#B$BBBBCC)CACHCiCwCxCCCCCCCDD1D\DiDjEEE:E[EiEjEEEEEEEEF#FNF[F\G G G4GUGcGdGGGGGGGGHHIHVHWIII,IVI_IhIuIIIIJJJJJJKK7KCK[K\K{KKKKKKL L5LBLCLLLM"MGMSMkMtMMMMMMMNNN&N3N^NNNO8O9O`OOOOOOOPPPPPJPuPPQ.Q/QVQwQQQQQQQQR RRARlRyRzS3S4S[S|SSSSSSTTTTTFTqT~TU,U-UTUUUUUUUVV9VFVGWWWW9W^WjWWWWWWWWXX/X0X=XJXuXXXYFYGYnYYYYYYYZZZ Z-ZXZZZ[6[7[^[[[[[[[\\\\\H\s\\]+],]S]t]]]]]]]]^^^=^h^u^v__ _G_r_{_____``,`9`:```aa=aIaaalaaaaaaabbbb,bWbbbc2c3cZc{cccccccdd ddEdpd}d~e(e)ePeqeeeeeeeefff;fffsftgggBgcgqgrggggggghh.hYhfhgi.i/iViwiiiiiiiijj j7j8jEjRj}jjjjjjkk kkk$k%k.kYkZkhkikjkkklkmkkkkkl l ll%lPl{llm3m4m[mmmmmmmnnAnNnWnXooooCohotoooooooop!p*p3p@pkplpypppppqqqqqqrrr&r3r^r_rlryrrrrsssssstttt&tQtRt_tlttttuuuuuuvvvv'vRvSv`vmvvvvwjwkwwwwwxxxx!xLxwxxxxxxxxxxyy,y5y>yKyvyyyyyyzzzzzz{{{={K{L{w{{{{{{{||2|?|@||}}1}?}@}k}t}}}}}}}}~&~3~<~=~~;IJu~ڀ0=>5CDpyɁւ,9:قڃ"01]fo|ƒ%&Ąń HQZg؆ +VcpƇӇԈÈ ?3ABmvŋҋ(56ތߍ'56ajsǍ*+ߎ(67cluȏ+,ӐԐ*+W`iv ߓ;DMZ˓Ĕ )2?klyܕꖮ֗ !LYfɗʘޘ0>?ir{Ι$12ۚܛ$23]fo|›%&֜ל-.Xajw !ɞʞ !KT]j۠ɠʠ !KT]jۢ=FO\ͣޥ'R_l¥ϥЦ