Probability for Financial Machine Learning
เป็นที่รู้กันดีว่าโอกาส ความเสี่ยง ความผันผวน หรือที่เรียกรวมกันว่าความไม่แน่นอนนั้น เป็นตัวละครสำคัญในโลกของการเงิน ทฤษฎีความน่าจะเป็นเป็นเครื่องมือที่ดีในการเข้าใจความไม่แน่นอนของมนุษยชาติในปัจจุบัน ดังนั้นเมื่อเราพยายามจะจัดการกับความไม่แน่นอน กระบวนการที่สร้างขึ้นมานั้นก็ล้วนแต่มีฐานมาจากทฤษฎีความน่าจะเป็นทั้งสิ้น ไม่ว่าจะเป็นสถิติ Time Series Analysis กระบวนการ Stochastic หรือแม้แต่ Machine Learning ก็ตาม
*หมายเหตุ : คอร์สนี้สอนทฤษฎี Probability & Statistic สำหรับต่อยอดในงานสาย Finance และอื่น ๆ ที่เกี่ยวข้อง
Agenda Course (ความยาวคอร์ส 31 ชั่วโมง)
📍Basic Knowledge
1. Introduction
2. Sets and set operations
3. Functions and graphing
4. Sequences and series
5. Counting technique
📍Probability
6. Sample spaces and events
7. Properties of probability
8. Conditional probability
10. Independence of event
12. Bayes theorem
📍Random Variables
13. Discrete random variables
14. Expectation and variance of discrete random variables
15. The Bernoulli distribution
16. Th Binomial distribution
17. The Geometric distribution
18. The Poisson distribution
19. Basic calculus
20. Continuous random variables
21. Expectation and variance of continuous random variables
22. Random number generation by rejection method
23. The Uniform distribution
24. Random number generation by inversion method
25. The Cauchy distribution
26. The Normal distribution
27. The Exponential distribution
28. The Poisson Process
📍Random Variables (cont)
29. Basic multivariate calculus
30. Joint distributions
31. Marginal distributions
32. Covariance and correlation
33. Conditional distributions
34. Independence of random variables
35. The Multinormal distribution
📍Random Sampling
36. Functions of random variables
37. Moment generating function
38. Sampling distributions
39. Law of Large Number
40. Central Limit Theorem
41. The t distributions
42. Distributions of mean
43. The Chi-square distributions
44. Distributions of variance
📍Estimation
45. Point estimator
46. Unbiased estimator
47. Consistent estimator
48. Asymptotically Normal estimator
49. Moment method
50. Quantile and median
51. Maximum Likelihood method
52. Confident interval
53. Asymptotic confidence interval
54. Sample size
55. Variance reduction
📍Hypothesis Testing
56. Statistical hypotheses
57. Errors
58. The critical region, significant level and power of the test
59. Neyman-Pearson lemma
60. Likelihood ratio test
61. Test of parameters
62. Test of two sample parameters
63. p-value
64. Test of goodness of fit
65. Test of independence
66. Test of identically distributed
📍Hypothesis Testing (cont)
67. Empirical distribution
68. Test of distribution choice
69. Test of normality
70. Regression
Course Features
- Lectures 65
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 34
- Assessments Yes
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Document
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Part1: Theory
- 1. Introduction
- 2. Basic Knowledge-Set and Set Operations
- 3. Basic Knowledge-Functions and Graphing
- 4. Basic Knowledge-Seauences and Sevies
- 5. Basic Knowledge-Counting Technique
- 6. Probability-Sample Spaces and Events
- 7. Probability-Properties of Probability
- 8. Probability-Conditional Probability
- 9. Probability-Bayes’ Theorem
- 10. Probability-Independen of Event
- 11. Random Variables – Discrete Random Variables
- 12. Random Variables – Expectation and Variance of Discrete Random
- 13. Random Variables – The Bernoulli Distribution
- 14. Random Variables – The Binomial Distribution
- 15. Random Variables – The Geometric Distribution
- 16. Random Variables – The Poisson Distribution
- 17. Random Variables – Continuous Random Variables
- 18. Random Variables – Expectation and Variance of Continuous Random
- 19. Random Variables – The Uniform Distribution
- 20. Random Variables – Random Number Generation by Rejection Method and Inversion Method
- 21. Random Variables – The Normal Distribution
- 22. Random Variables – The Exponential Distribution
- 23. Random Variables – The Poisson Process
- 24. Random Variables – Recap and Overview
- 25. Random Variables – Random Walk
- 26. Random Variables – Joint Distributions
- 27. Random Variables – Marginal Distributions
- 28. Random Variables – Conditional Distribution
- 29. Random Variables – Independence of Random Variables
- 30. Random Variables – Covariance and Correlation
- 31. Random Sampling – Random Sampling
- 32. Random Sampling – Moment Generating Function
- 33. Random Sampling – Sampling Distributions
- 34. Random Sampling – Central Limit Theorem
- 35. Random Sampling – Distribution of Variance
- 36. Random Sampling – Conclusion
- 37. Estimation – Point Estimator
- 38. Estimation – Unbiased Estimator
- 39. Estimation – Consistent Estimator
- 40. Estimation – Asymptotically Normal Estimator
- 41. Estimation – Moment Method
- 42. Estimation – Maximum Likelihood Method
- 43. Estimation – Bayesian Method
- 44. Estimation – Confident Interval
- 45. Estimation – Sample Size
- 46. Hypothesis Testing – Statistical hypotheses
- 47. Hypothesis Testing – Two tails test
- 48. Hypothesis Testing – One tails test
- 49. Hypothesis Testing – p-value
- 50. Hypothesis Testing – Goodness-of-fit test
- 51. Hypothesis Testing – Jarque-Bera normality test
- 52. Hypothesis Testing – Test of Independence and homogeneity
- 53. Hypothesis Testing – Kolomogorov-Smirnov test
- 54. Hypothesis Testing – งานวิจัยที่มีรากฐานจาก Prob
- 55. Hypothesis Testing – Linear regression
- 56. Hypothesis Testing – MLE of coefficients
- 57. Hypothesis Testing – Correlation
- 58. Hypothesis Testing – Extension
- 59. Hypothesis Testing – Nonprametric Test
- 60. Hypothesis Testing – Sign test
- 61. Hypothesis Testing – Wilcoxon sign-rank test
- 62. Hypothesis Testing – Mann-Whitney U-test and KrusKruskal-Wallis H-test
- 63. Hypothesis Testing – Run test
- 64. Hypothesis Testing – Conclusion of Probability for Financial Machine Learning
