Neural Networks Basics for Finance & Accounting Management

This course provides a foundational understanding of Neural Networks, a core technology in Artificial Intelligence (AI), with a focus on their applications within the Finance & Accounting Management department. You'll explore how these powerful algorithms can learn from data and solve complex financial problems, such as fraud detection, risk assessment, and even financial forecasting. 

• Grasp the fundamental concepts of Artificial Neural Networks and their structure (neurons, layers, activation functions).

• Understand different types of neural networks relevant to financial tasks (e.g., Perceptrons, Multi-Layer Perceptrons, Convolutional Neural Networks).

• Learn how neural networks learn and improve through training with financial data.

• Explore common applications of neural networks in Finance & Accounting Management (e.g., credit risk assessment, loan approval prediction).

• Gain hands-on experience implementing basic neural networks using popular Python libraries (e.g., TensorFlow, PyTorch).

• Develop the ability to interpret the results of neural network models for effective decision-making in finance.

1. Introduction to Artificial Intelligence and Neural Networks:

• The landscape of Artificial Intelligence and its subfields.

• Demystifying Neural Networks: structure, biological inspiration, and learning process.

• Understanding the terminology: neurons, activation functions, loss functions, and optimization algorithms.

• Real-world applications of Neural Networks in the financial domain.

2. Building and Training Neural Networks:

• Hands-on experience with building simple neural networks using a user-friendly Python library.

• Exploring different types of neural networks (Perceptrons, Multi-Layer Perceptrons) and their suitability for financial tasks.

• Training neural networks with financial data: understanding the training process and hyperparameter tuning.

• Hands-on coding exercise: training a neural network to classify financial transactions (e.g., fraudulent vs. legitimate).

3. Advanced Architectures and Financial Applications:

• Introduction to Convolutional Neural Networks (CNNs) for image analysis in finance (e.g., analyzing financial documents).

• Exploring Recurrent Neural Networks (RNNs) for sequence data in finance (e.g., time series forecasting).

• Leveraging neural networks for financial tasks like credit risk assessment, loan approval prediction, and algorithmic trading (basic introduction).

• Case studies: Examining real-world implementations of neural networks for financial tasks.

4. Evaluation, Interpretation, and Future Trends:

• Understanding how to evaluate the performance of neural network models in finance (e.g., accuracy, precision, recall).

• Interpreting the results of neural networks and explaining their predictions for informed decision-making.

• Limitations and considerations for using neural networks in financial applications.

• Emerging trends and future directions in Neural Networks for Finance & Accounting Management.

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques