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2019年6月6日

[Knowledge-based AI] {ud409} Lesson 8: 08 - Learning by Recording Cases

摘要： Optional Readings : Learning by Recording Cases Case Retrieval by Nearest Neighbor Nearest Neighbor in k-Dimensional Space learning and memory(recordi 阅读全文

posted @ 2019-06-06 06:00 ecoflex 阅读(142) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 7: 07 - Frames

摘要： Optional reading: Winston Chapter 9, pages 179-182, 202-206 compare frame with sematic network and production system How do we make sense of a sentenc 阅读全文

posted @ 2019-06-06 05:43 ecoflex 阅读(106) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 6: 06 - Production Systems

摘要： This example is adapted from the following paper: Lehman, J. F., Laird, J. E., & Rosenbloom, P. S. (1996). A gentle introduction to Soar, an architect 阅读全文

posted @ 2019-06-06 05:11 ecoflex 阅读(227) 评论(0) 推荐(0)

2019年6月5日

[Knowledge-based AI] {ud409} Lesson 5: 05 - Means-Ends Analysis

摘要： Optional Reading : Exercise: The Block Problem This example is adapted from the following book: Winston, P. (1993). Artificial Intelligence (3rd ed.). 阅读全文

posted @ 2019-06-05 21:58 ecoflex 阅读(257) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 4: 04 - Generate & Test

摘要： Optional reading: Winston Chapter 3, pp. 47-50 knowledge representation, problem solving (going to be discussed) , architectures Dumb Generators, Dumb 阅读全文

posted @ 2019-06-05 21:42 ecoflex 阅读(168) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 3: 03 - Semantic Networks

摘要： Knowledge Representations eg. F=ma Introduction to Semantic Networks The example above is adapted from Raven's test of progressive matrices. For furth 阅读全文

posted @ 2019-06-05 14:16 ecoflex 阅读(264) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 2: 02 - Introduction to CS7637

摘要： Optional Readings : Introduction to Computational Psychometrics Putting Online Learning and Learning Sciences Together: https://www.youtube.com/watch? 阅读全文

posted @ 2019-06-05 12:08 ecoflex 阅读(209) 评论(0) 推荐(0)

[Knowledge-based AI] {ud409} Lesson 1: 01 - Intro to Knowledge-Based AI

摘要： Characteristics of AI Problems Characteristics of AI Agents What is Knowledge-Based AI? Optional Reading: "What is AI anyway?" - Ashok K. Goel, Jim Da 阅读全文

posted @ 2019-06-05 11:15 ecoflex 阅读(316) 评论(0) 推荐(0)

[Software Development Process] {ud805} excerpt

摘要： Introduction: What is software engineering? Why do we need it? Importance of Software Engineering Evidence of the Software Crisis You can access the p 阅读全文

posted @ 2019-06-05 10:46 ecoflex 阅读(310) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna

摘要： The RL problem Trading as an RL problem Mapping trading to RL Markov decision problems Unknown transitions and rewards What to optimize? Learning Proc 阅读全文

posted @ 2019-06-05 08:17 ecoflex 阅读(280) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 23: 03-03 Assessing a learning algorithm | Lesson 24: 03-04 Ensemble learners, bagging and boosting

摘要： A closer look at KNN solutions What happens as K varies What happens as D varies Metric 1 RMS Error In Sample vs out of sample Which is worse? Cross v 阅读全文

posted @ 2019-06-05 07:41 ecoflex 阅读(162) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 21: 03-01 How Machine Learning is used at a hedge fund | Lesson 22: 03-02 Regression

摘要： a data-centric way to build predictive models The ML problem Supervised regression learning Robot navigation example How it works with stock data Exam 阅读全文

posted @ 2019-06-05 07:17 ecoflex 阅读(138) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 19: 02-09 The Fundamental Law of active portfolio management | Lesson 20: 02-10 Portfolio optimization and the efficient frontier

摘要： this lesson => Buffet said two things => (1) investor skill => (2) breadth / the number of investments Grinold's Fundamental Law breadth => more oppor 阅读全文

posted @ 2019-06-05 06:34 ecoflex 阅读(411) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 17: 02-07 Dealing with Data | Lesson 18: 02-08 Efficient Markets Hypothesis

摘要： How data is aggregated tick interval can be day, mintue, ... etc. Price anomaly Stock splits Split adjustment Dividends Adjusting for dividends the cl 阅读全文

posted @ 2019-06-05 05:03 ecoflex 阅读(138) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 15: 02-05 How hedge funds use the CAPM | Lesson 16: 02-06 Technical Analysis

摘要： Risks for hedge funds Two stock scenario Two stock CAPM math how to remove the incluence of the market or beta??? Allocations remove market risk How d 阅读全文

posted @ 2019-06-05 04:23 ecoflex 阅读(150) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 13: 02-03 What is a company worth? | Lesson 14: 02-04 The Capital Assets Pricing Model (CAPM)

摘要： What is a company worth? Yes, the value of $1 would typically reduce over time, and the sum of the individual $1's generated every year would converge 阅读全文

posted @ 2019-06-05 03:00 ecoflex 阅读(215) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 11: 02-01 So you want to be a hedge fund manager? | Lesson 12: 02-02 Market Mechanics

摘要： Computational Investing MC2: Computational Investing Lessons: In this mini-course, we focus on modeling the behavior of stock markets. Projects: Types 阅读全文

posted @ 2019-06-05 01:20 ecoflex 阅读(135) 评论(0) 推荐(0)

2019年6月4日

[Machine Learning for Trading] {ud501} Lesson 9: 01-08 Optimizers: Building a parameterized model | Lesson 10: 01-09 Optimizers: How to optimize a portfolio

摘要： What is an optimizer? Minimization example How to defeat a minimizer Convex problems Building a parameterized model Minimizer finds coefficients What 阅读全文

posted @ 2019-06-04 12:12 ecoflex 阅读(111) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 7: 01-06 Histograms and scatter plots | Lesson 8: 01-07 Sharpe ratio and other portfolio statistics

摘要： A closer look at daily returns Histogram of daily returns gaussian => kurtosis = 0 How to plot a histogram Computing histogram statistics Select the o 阅读全文

posted @ 2019-06-04 10:59 ecoflex 阅读(193) 评论(0) 推荐(0)

[Machine Learning for Trading] {ud501} Lesson 5: 01-04 Statistical analysis of time series | Lesson 6: 01-05 Incomplete data

摘要： Lesson outline Pandas makes it very convenient to compute various statistics on a dataframe: Global statistics: mean, median, std, sum, etc. [more] Ro 阅读全文

posted @ 2019-06-04 09:31 ecoflex 阅读(263) 评论(0) 推荐(0)

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