Unsupervised Learning: Embeddings for Finance & Accounting Management 

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. 

• 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.

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.

• 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