Machine-Learning-in-Fintech

2019 Summer Course in ZJU: Contents: Notes & Assignments & my independent project

Key Job: 实验3:实现聚类和分类

实验4:NLP in Fintech

实验5:实现马科维茨投资组合策略（MV）

实验7:利⽤常用的分类模型(包括感知机，SVM，朴素⻉叶斯，决策树，logistic回归， random forest等等)，在训练集上进⾏训练。

实验8:大作业 个人project

Clustering

Learned k-means, k-medoids and Spectral clustering, realize them in specific datasets.

1. Application of clustering in finance;
2. Criteria to measure the quality of clustering (maximize the distance between classes and minimize the distance within classes);
3. Expression of data distance;
4. Segmentation based clustering (k-means, k-medoids);
5. Spectral clustering (Spectral clustering).

Classification

1. Application of classification in finance;
2. Confusion matrix;
3. Linear regression;
4. Machine learning classifier (SVM);
5. Deep learning classifier (neural networks).

NLP in Financial Scenario

Natural Language Processing

Assignment:

Financial text data were processed with natural language and the results were evaluated and analyzed.

quadratic programming & reinforcement learning

1. Intelligent investment consulting technology:

   big data fusion, investment user portrait, quantification, investment portfolio, risk control, intelligent customer service

2. Financial theory of intelligent investment consultants:

   markowitz portfolio theory and market equilibrium theory

3. Application of quadratic programming in portfolio algorithm

4. Application of reinforcement learning in portfolio algorithm

Assignment:

Implement a portfolio algorithm