Unsupervised Learning: Embeddings for Finance & Accounting Management
Course Overview:
This course delves into the world of unsupervised learning, specifically focusing on the concept of embeddings and their applications within the Finance & Accounting Management department. Embeddings are a powerful technique for transforming complex data (like text or transactions) into a lower-dimensional space while preserving important relationships. This course will equip you with the knowledge and skills to leverage embeddings for various financial tasks.
Learning Objectives:
Understand the core principles of unsupervised learning and its role in financial applications.
Grasp the concept of embeddings and their ability to capture semantic relationships within data.
Explore popular embedding techniques like Word2Vec and GloVe.
Learn how to implement and train embedding models using Python libraries.
Apply embedding techniques to solve real-world financial problems like anomaly detection, fraud analysis, and customer segmentation.
Evaluate and interpret the results of embedding models for effective decision-making.
Course Highlights:
1. Introduction to Unsupervised Learning & Embeddings:
The unsupervised learning landscape in finance.
Concept of data embeddings and their benefits.
Exploring different types of embeddings (word embeddings, transaction embeddings).
Real-world use cases of embeddings in finance (e.g., fraud detection, customer churn prediction).
2. Popular Embedding Techniques:
Deep dive into Word2Vec and its variations (Skip-gram, CBOW).
Understanding GloVe and its advantages for financial text data.
Hands-on coding exercise: Implementing Word2Vec for representing financial terms.
3. Building and Training Embedding Models:
Python libraries for embedding: Gensim, TensorFlow.
Pre-processing financial text data for embedding models.
Training embedding models on financial datasets.
Hands-on coding exercise: Training a Word2Vec model on financial news articles.
4. Applications & Evaluation in Finance:
Leveraging embeddings for anomaly detection in financial transactions.
Customer segmentation and risk analysis using embeddings.
Evaluating the effectiveness of embedding models for financial tasks.
Case studies: Applying embeddings to real-world financial problems.
Prerequisites:
Solid understanding of linear algebra, calculus, and probability theory
Proficiency in programming with Python, including experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
Familiarity with unsupervised learning concepts and dimensionality reduction techniques