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Machine Learning 72
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DocumentLecture1.1
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Week 1 – IntroductionLecture1.2
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Week 1 – K Nearest Neighbor : Theory : What is K Nearest Neighbor ?Lecture1.3
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Week 1 – K Nearest Neighbor : Theory : StateLecture1.4
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Week 1 – K Nearest Neighbor : Theory : How to create modelLecture1.5
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Week 1 – K Nearest Neighbor : Theory : ตอบคำถามนักเรียนLecture1.6
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Week 1 – K Nearest Neighbor : Theory : ตัวอย่างการคำนวณ KNN-CLecture1.7
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Week 1 – K Nearest Neighbor : Theory : When will we use KNN-C ?Lecture1.8
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Week 1 – K Nearest Neighbor : Code : Overview & Import LibraryLecture1.9
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Week 1 – K Nearest Neighbor : Code : NormalizationLecture1.10
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Week 1 – K Nearest Neighbor : Code : วัดระยะห่างLecture1.11
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Week 1 – K Nearest Neighbor : Code : เรียงลำดับ Target ตามระยะห่างLecture1.12
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Week 1 – K Nearest Neighbor : Code : หาผลลัพธ์จากเพื่อนบ้าน K ตัวLecture1.13
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Week 1 – K Nearest Neighbor : Code : หา K ที่ดีที่สุดLecture1.14
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Week 1 – K Nearest Neighbor : Code : เรียนรู้ & พยากรณ์ Part 1Lecture1.15
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Week 1 – K Nearest Neighbor : Code : เรียนรู้ & พยากรณ์ Part 2Lecture1.16
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Week 1 – K Nearest Neighbor for Regression : Theory & Code & ExampleLecture1.17
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Week 1 – K Nearest Neighbor for Regression : Theory : เฉลยการบ้านLecture1.18
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Week 1 – เฉลยการบ้าน K Nearest Neighbor for RegressionLecture1.19
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Week 2 – Naive Bayes : Theory : Bayes’ TheoremLecture1.20
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Week 2 – Naive Bayes : Theory : What is Naive Bayes ?Lecture1.21
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Week 2 – Naive Bayes : Theory : StateLecture1.22
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Week 2 – Naive Bayes : Theory : How to create modelLecture1.23
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Week 2 – Naive Bayes : Theory : ตัวอย่างการคำนวณ Naive BayesLecture1.24
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Week 2 – Naive Bayes : Theory : ที่มาสมการ Naive BayesLecture1.25
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Week 2 – Naive Bayes : Code : Overview & Import LibraryLecture1.26
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Week 2 – Naive Bayes : Code : สร้าง Dictionary สำหรับเตรียมนับสมาชิกและเตรียมเก็บความน่าจะเป็นLecture1.27
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Week 2 – Naive Bayes : Code : นับจำนวนสมาชิกจาก Training Set เพื่อใส่ใน Count_MatrixLecture1.28
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Week 2 – Naive Bayes : Code : คำนวณความน่าจะเป็นจาก Count_Matrix เพื่อใส่ใน Prob_Matrix & เรียนรู้Lecture1.29
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Week 2 – Naive Bayes : Code : พยากรณ์Lecture1.30
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Week 2 – Naive Bayes : Code : Read Data & Prepare Data, Create Model, Making PredictionsLecture1.31
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Week 2 – Naive Bayes : ExampleLecture1.32
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Week 2 – Naive Bayes : TheoryLecture1.33
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Week 2 – Naive Bayes : ตัวอย่างการคำนวณ Naive Bayes Continuous DataLecture1.34
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Week 2 – Naive Bayes : CodeLecture1.35
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Week 2 – Naive Bayes : ExampleLecture1.36
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Week 3 – Decision Tree : Theory : IntroductionLecture1.37
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Week 3 – Decision Tree : Theory : What is Decision Tree ?Lecture1.38
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Week 3 – Decision Tree : Theory : StateLecture1.39
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Week 3 – Decision Tree : Theory : How to create modelLecture1.40
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Week 3 – Decision Tree : Theory : ตัวอย่างการคำนวณ Decision TreeLecture1.41
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Week 3 – Decision Tree : Theory : ที่มาของ GiniLecture1.42
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Week 3 – Decision Tree : ตัวอย่างการเขียน Code RecursiveLecture1.43
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Week 3 – Decision Tree : Code Part 1Lecture1.44
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Week 3 – Decision Tree : Code Part 2Lecture1.45
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Week 3 – Random Forest (1)Lecture1.46
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Week 3 – Random Forest (2)Lecture1.47
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Week 4 – SVM : Theory : IntroductionLecture1.48
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Week 4 – SVM : Theory : Method of Lagrange MultipliersLecture1.49
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Method of Lagrange Multipliers Part 1Lecture1.50
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Method of Lagrange Multipliers Part 2Lecture1.51
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Week 4 – SVM : Theory : Karush Kuhn TuckerLecture1.52
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Karush Kuhn Tucker Part 1Lecture1.53
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Karush Kuhn Tucker Part 2Lecture1.54
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Week 4 – SVM : Theory : เฉลยการบ้าน Method of Lagrange Multipliers ข้อ 1Lecture1.55
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Week 4 – SVM : Theory : เฉลยการบ้าน Karush Kuhn Tucker ข้อ 2Lecture1.56
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Week 4 – SVM : Theory : What is SVM ? & StateLecture1.57
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Week 4 – SVM : Theory : How to create model Part 1Lecture1.58
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Week 4 – SVM : Theory : How to create model Part 2Lecture1.59
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Week 4 – SVM : Theory : How to create model Part 3Lecture1.60
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Week 4 – SVM : Theory : How to create model Part 4Lecture1.61
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Week 4 – SVM : Theory : How to create model Part 5Lecture1.62
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Week 4 – SVM : Theory : SMOLecture1.63
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ SMO Part 1Lecture1.64
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Week 4 – SVM : Theory : ตัวอย่างการคำนวณ SMO Part 2Lecture1.65
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Week 4 – SVM : Code & ExampleLecture1.66
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Week 5 – สรุปเนื้อหาคอร์ส Part 1Lecture1.67
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Week 5 – สรุปเนื้อหาคอร์ส Part 2Lecture1.68
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Week 5 – Workshop 1Lecture1.69
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Week 5 – Workshop 2 Part 1Lecture1.70
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Week 5 – Workshop 2 Part 2Lecture1.71
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Week 5 – ปิดคอร์ส Machine LearningLecture1.72
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Deep Learning the Series 101
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Week 1 – WelcomeLecture2.1
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Week 1 – AI OverviewLecture2.2
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Week 1 – IntroductionLecture2.3
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Week 1 – Model CreationLecture2.4
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Week 1 – ตอบคำถาม SSE มีไว้ใช้ทำอะไร?Lecture2.5
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Week 1 – Data PreparationLecture2.6
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Week 2 – Recap Week 1Lecture2.7
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Week 2 – Model Evaluation for RegressionLecture2.8
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Week 2 – Basic Workshop – Code PipelineLecture2.9
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Week 2 – Basic Workshop – AI in MarketingLecture2.10
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Week 2 – Basic Workshop – AI in InvestmentLecture2.11
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Week 2 – Basic Workshop – Smart FarmLecture2.12
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Week 2 – ตอบคำถามนักเรียนLecture2.13
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Week 2 – Basic Workshop – AI in Rental BusinessLecture2.14
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Week 2 – Basic Workshop – AI in InsuranceLecture2.15
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Week 3 – Model Improvement – Strong AssumptionLecture2.16
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Week 3 – Model Improvement – Weak AssumptionLecture2.17
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Week 3 – Model Improvement – Problem with Linearly DependentLecture2.18
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Week 3 – ตอบคำถาม ข้อเสียของการ drop one hotLecture2.19
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Week 3 – Model Improvement – SolutionLecture2.20
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Week 3 – Model Improvement – RegularizationLecture2.21
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Week 3 – ตอบคำถาม RegularizationLecture2.22
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Week 3 – Cross ValidationLecture2.23
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Week 3 – Data Preparation – Feature ScalingLecture2.24
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Week 3 – Advanced WorkshopLecture2.25
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Week 4 – RoadmapLecture2.26
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Week 4 – DL102 – IntroductionLecture2.27
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Week 4 – LoR (2-class) – IntroductionLecture2.28
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Week 4 – LoR (2-class) – DataLecture2.29
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Week 4 – LoR (2-class) – Model Creation part1Lecture2.30
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Week 4 – LoR (2-class) – Model Creation part2Lecture2.31
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Week 4 – LoR (2-class) – PredictionLecture2.32
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Week 4 – LoR (2-class) – ImprovementLecture2.33
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Week 4 – Model Evaluation for ClassificationLecture2.34
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Week 4 – Workshop LoR (2-class) – Code PipelineLecture2.35
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Week 4 – Workshop LoR (2-class) – AI in HealthcareLecture2.36
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Week 4 – Workshop LoR (2-class) – AI in AgricultureLecture2.37
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Week 4 – Workshop LoR (2-class) – Fake NewsLecture2.38
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week 5 – Recap – LoR (2-class)Lecture2.39
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Week 5 – LoR (multi-class) – เกริ่นนำLecture2.40
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Week 5 – LoR (multi-class) – Introduction – LoR with MulticlassLecture2.41
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Week 5 – LoR (multi-class) – Introduction – Why Softmax?Lecture2.42
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Week 5 – LoR (multi-class) – Introduction – Extension to Neural NetworkLecture2.43
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Week 5 – LoR (multi-class) – Introduction – Real World ApplicationLecture2.44
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Week 5 – ตอบคำถาม sigmoid vs softmaxLecture2.45
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Week 5 – LoR (multi-class) – DataLecture2.46
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Week – LoR (multi-class) – ModelLecture2.47
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Week 5 – LoR (multi-class) – PredictionLecture2.48
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Week 5 – LoR (multi-class) – ImprovementLecture2.49
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Week 5 – Workshop LoR (multi-class) – AI in Fruit IndustryLecture2.50
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Week 5 – Workshop LoR (multi-class) – AI in News CategorizationLecture2.51
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Week 5 – Cross EntropyLecture2.52
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Week 6 – RoadmapLecture2.53
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Week 6 – DL103 – IntroductionLecture2.54
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Week 6 – NN&DL – What is NN?Lecture2.55
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Week 6 – NN&DL – Why we need NN?Lecture2.56
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Week 6 – NN&DL – Real World ApplicationLecture2.57
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Week 6 – NN&DL – Architecture of NNLecture2.58
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Week 6 – NN&DL – Component of NNLecture2.59
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Week 6 – NN&DL – How NN WorkLecture2.60
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Week 6 – ตอบคำถาม decision boundary เปลี่ยนแปลงตามเวลาไหม?Lecture2.61
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Week 6 – NN&DL – What is DL?Lecture2.62
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Week 6 – NN&DL – Architecture of DLLecture2.63
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Week 6 – NN&DL – Component of DLLecture2.64
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Week 6 – NN&DL – Why we need DL?Lecture2.65
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Week 6 – NN&DL – How DL WorkLecture2.66
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Week 6 – DL for RegressionLecture2.67
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Week 6 – DL for Binary ClassificationLecture2.68
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Week 6 – DL for Multiclass ClassificationLecture2.69
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Week 7 – Recap – NN&DLLecture2.70
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Week 7 – DL Workshop – WorkflowLecture2.71
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Week 7 – DL Workshop – OverviewLecture2.72
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Week 7 – DL Workshop – Code Pipeline Part1Lecture2.73
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Week 7 – DL Workshop – Code Pipeline Part2Lecture2.74
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Week 7 – DL Workshop – Code Pipeline Part3Lecture2.75
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Week 7 – DL Workshop – AI in Civil EngineeringLecture2.76
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Week 7 – DL Workshop – AI in HealthcareLecture2.77
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Week 7 – DL Workshop – AI in Skin CancerLecture2.78
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Week 7 – ตอบคำถาม underfit & overfit คืออะไร?Lecture2.79
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Week 7 – DL Interpretation – 1st Hidden LayerLecture2.80
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Week 7 – DL Interpretation – 2nd Hidden LayerLecture2.81
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Week 7 – DL Interpretation – 3rd Hidden Layer & ConclusionLecture2.82
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Week 7 – DL Interpretation – Conclusion & AdaptationLecture2.83
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Week 8 – Recap – DL InterpretationLecture2.84
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Week 8 – Improvement of DL – OverviewLecture2.85
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Week 8 – Speed Up with GPU – What is GPU?Lecture2.86
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Week 8 – Speed Up with GPU – How GPU Accelerate DL?Lecture2.87
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Week 8 – Speed Up with GPU – Welcome to ColabLecture2.88
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Week 8 – Speed Up with GPU – Train Model with GPULecture2.89
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Week 8 – Imbalanced ClassLecture2.90
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Week 8 – L2, L1, Elastic NetLecture2.91
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Week 8 – Dropout Regularization part1Lecture2.92
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Week 8 – Dropout Regularization part2Lecture2.93
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Week 8 – Gradient Descent VariantsLecture2.94
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Week 8 – MomentumLecture2.95
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Week 8 – Adagrad & RMSProp & AdamLecture2.96
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Week 8 – Advanced Workshop – Code PipelineLecture2.97
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Week 8 – Advanced Workshop – AI in Real Estate BusinessLecture2.98
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Week 8 – Advanced Workshop – AI in Diagnosing Alzheimer’sLecture2.99
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Week 8 – Advanced Workshop – AI in Speech RecognitionLecture2.100
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Week 8 – สรุปคอร์ส DL the SeriesLecture2.101
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Probability for Financial Machine Learning 65
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DocumentLecture3.1
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Week 1 – IntroductionLecture3.2
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Week 1 – Basic Knowledge – Set and Set OperationsLecture3.3
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Week 1 – Basic Knowledge – Functions and GraphingLecture3.4
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Week 1 – Basic Knowledge – Sequences and SeriesLecture3.5
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Week 1 – Basic Knowledge – Counting TechniqueLecture3.6
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Week 2 – Probability – Sample Spaces and EventsLecture3.7
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Week 2 – Probability – Properties of ProbabilityLecture3.8
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Week 2 – Probability – Conditional ProbabilityLecture3.9
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Week 2 – Probability – Bayes’ TheoremLecture3.10
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Week 2 – Probability – Independent of EventLecture3.11
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Week 3 – Random Variables – Discrete Random VariablesLecture3.12
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Week 3 – Random Variables – Expectation and Variance of Discrete RandomLecture3.13
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Week 3 – Random Variables – The Bernoulli DistributionLecture3.14
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Week 3 – Random Variables – The Binomial DistributionLecture3.15
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Week 3 – Random Variables – The Geometric DistributionLecture3.16
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Week 3 – Random Variables – The Poisson DistributionLecture3.17
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Week 3 – Random Variables – Continuous Random VariablesLecture3.18
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Week 3 – Random Variables – Expectation and Variance of Continuous RandomLecture3.19
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Week 3 – Random Variables – The Uniform DistributionLecture3.20
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Week 3 – Random Variables – Random Number Generation by Rejection Method and Inversion MethodLecture3.21
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Week 3 – Random Variables – The Normal DistributionLecture3.22
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Week 3 – Random Variables – The Exponential DistributionLecture3.23
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Week 3 – Random Variables – The Poisson ProcessLecture3.24
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Week 3 – Random Variables – Recap and OverviewLecture3.25
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Week 3 – Random Variables – Random WalkLecture3.26
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Week 3 – Random Variables – Joint DistributionsLecture3.27
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Week 3 – Random Variables – Marginal DistributionsLecture3.28
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Week 3 – Random Variables – Conditional DistributionLecture3.29
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Week 3 – Random Variables – Independence of Random VariablesLecture3.30
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Week 3 – Random Variables – Covariance and CorrelationLecture3.31
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Week 4 – Random Sampling – Random SamplingLecture3.32
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Week 4 – Random Sampling – Moment Generating FunctionLecture3.33
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Week 4 – Random Sampling – Sampling DistributionsLecture3.34
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Week 4 – Random Sampling – Central Limit TheoremLecture3.35
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Week 4 – Random Sampling – Distribution of VarianceLecture3.36
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Week 4 – Random Sampling – ConclusionLecture3.37
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Week 5 – Estimation – Point EstimatorLecture3.38
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Week 5 – Estimation – Unbiased EstimatorLecture3.39
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Week 5 – Estimation – Consistent EstimatorLecture3.40
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Week 5 – Estimation – Asymptotically Normal EstimatorLecture3.41
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Week 5 – Estimation – Moment MethodLecture3.42
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Week 5 – Estimation – Maximum Likelihood MethodLecture3.43
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Week 5 – Estimation – Bayesian MethodLecture3.44
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Week 5 – Estimation – Confident IntervalLecture3.45
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Week 5 – Estimation – Sample SizeLecture3.46
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Week 6 – Hypothesis Testing – Statistical hypothesesLecture3.47
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Week 6 – Hypothesis Testing – Two tails testLecture3.48
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Week 6 – Hypothesis Testing – One tails testLecture3.49
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Week 6 – Hypothesis Testing – p-valueLecture3.50
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Week 6 – Hypothesis Testing – Goodness-of-fit testLecture3.51
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Week 6 – Hypothesis Testing – Jarque-Bera normality testLecture3.52
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Week 6 – Hypothesis Testing – Test of Independence and homogeneityLecture3.53
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Week 6 – Hypothesis Testing – Kolomogorov-Smirnov testLecture3.54
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Week 6 – Hypothesis Testing – งานวิจัยที่มีรากฐานจาก ProbLecture3.55
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Week 6 – Hypothesis Testing – Linear regressionLecture3.56
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Week 6 – Hypothesis Testing – MLE of coefficientsLecture3.57
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Week 6 – Hypothesis Testing – CorrelationLecture3.58
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Week 6 – Hypothesis Testing – ExtensionLecture3.59
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Week 6 – Hypothesis Testing – Nonprametric TestLecture3.60
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Week 6 – Hypothesis Testing – Sign testLecture3.61
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Week 6 – Hypothesis Testing – Wilcoxon sign-rank testLecture3.62
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Week 6 – Hypothesis Testing – Mann-Whitney U-test and KrusKruskal-Wallis H-testLecture3.63
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Week 6 – Hypothesis Testing – Run testLecture3.64
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Week 6 – Hypothesis Testing – Conclusion of Probability for Financial Machine LearningLecture3.65
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Time Series Analysis and Stochastic Process 11
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DocumentLecture4.1
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Week 1 – Introduction & Time Series ModelLecture4.2
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Week 1 – Time Series ModelLecture4.3
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Week 2 – Statistics for Time Series Model (1)Lecture4.4
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Week 2 – Statistics for Time Series Model (2)Lecture4.5
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Week 3 – Other Models (ARCH/ Bilinear/ Piecewise Linear/ Multivariate) & Stochastic ProcessLecture4.6
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Week 3 – Stochastic ProcessLecture4.7
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Week 4 – Stochastic Calculus (1)Lecture4.8
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Week 4 – Stochastic Calculus (2)Lecture4.9
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Week 5 – Stochastic Numerical Methods (1)Lecture4.10
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Week 5 – Stochastic Numerical Methods (2)Lecture4.11
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Week 5 – Stochastic Numerical Methods (1)
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