This is an introductory course for managers, data scientists, and software engineers working in finance. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, and specific use cases. To help with the material absorption, the lectures are supplemented with the demos and a limited amount of lab work.
Students attending this course should have an interest in technology and machine learning, a penchant for hard work, and a readiness for questions.
1 Day/Lecture & Lab
This course is designed for managers, data scientists, and software engineers.
Introductions and overviews
- Linear regression
- Logistic Regression
- SVM (Supervised Vector Machines)
- Naive Bayes
- Decision Trees
- Clustering (K-Means)
- Principal Component Analysis (PCA)
- Recommendation (Collaborative filtering)
- Deep Learning and AI