Large Language Models

Large Language Models (LLMs) in Finance Certificate

The integration of Large Language Models (LLMs) into finance is revolutionizing the industry, offering new ways to process information, analyze data, and interact with customers. This course by the Artificial Intelligence Finance Institute delves into the essentials of LLMs, their practical applications in finance, and hands-on implementation techniques.

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Global Course Fee:
$2,000

Exclusive Discounted Wright Offer:
$1,000 $2,000
50% OFF

Time Commitment:
Time Commitment: 20 Hours | 9 Weeks

Start Date:
28th October - 17th January

Evaluation:
Final Project + Certificate

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info@aifinanceinstitute.com
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The integration of Large Language Models (LLMs) into finance is revolutionizing the industry, offering new ways to process information, analyze data, and interact with customers. This course by the Artificial Intelligence Finance Institute delves into the essentials of LLMs, their practical applications in finance, and hands-on implementation techniques. From understanding the architecture of LLMs to deploying them for financial analysis and customer service, participants will learn to harness the power of AI to innovate and improve efficiency in the financial sector. Whether you’re a finance professional, a developer, or a student, this course provides the knowledge and skills needed to navigate the future of finance with AI.

The Large Language Models (LLMs) in Finance Certificate will guide participants through the essentials of LLMs, including their architecture, operation, and the latest advancements in the field. It will delve into the practical aspects of deploying these models for financial tasks, such as fine-tuning for domain-specific applications, implementing retrieval-augmented generation for enhanced information processing, and evaluating model performance. Through a series of hands-on examples and projects, learners will gain the skills necessary to apply LLMs effectively within the finance sector, addressing real-world challenges and unlocking new opportunities.

Large Language Models (LLM’s) in Finance Certificate in partnership with The Artificial Intelligence Finance Institute’s (AIFI).

You will be able to receive up to 80 CPD points for completing this course.

This Large Language Models (LLMs) in Finance Certificate is certified by CPD Certification Service U.K.

The CPD Certification Service was established in 1996 as the independent CPD accreditation institution operating across industry sectors to complement the CPD policies of professional and academic bodies. The CPD Certification Service provides recognised independent CPD accreditation compatible with global CPD principles.

  • Introduction
  • LLMs Foundations
  • What is an LLM? (Overview, Transformer architecture, Encoder + Decoder Architecture, etc.)

  • Continuation of LLM Foundations
  • Examples of LLMs, Tokenization and Embedding
  • Limitations of LLMs
  • Knowledge Cutoff, Domain Specific Challenges, Solutions
  • Running LLM Locally

    • Prompt Engineering
    • Exploration of techniques such as zero-shot learning, few-shot learning, etc.
    • Fine-Tuning Introduction
    • Introduction to Fine-Tuning, PEFT

    • Introduction to Quantization
    • What is Quantization?
    • Benefits of Quantization in LLMs
    • Practical Examples of Quantization in Finance
    • Introduction to Sharding
    • What is Sharding?
    • How Sharding Optimizes LLM Performance
    • Implementing Sharding in Financial LLM Applications

    • Continuation of Fine-Tuning
    • RLHF, LoRa, QLoRa, Practical Examples
    • DPO,KTO
    • RAG (Retrieval-Augmented Generation)
    • RAG Explanation, Why to Use RAG?

    • Continuation of RAG
    • RAG Components, Last RAG Advancement Techniques, Practical Examples
    • Evaluation
    • Introduction to LLM Evaluation, Evaluation Metrics and Methods, Using Trulens

    • LLM Agents
    • Introduction to LLM Agents in Finance
    • Use Cases and Implementation Strategies
    • Building and Deploying LLM Agents for Financial Services
    • AI Safety
    • Understanding AI Safety
    • Tools and Strategies for AI Safety
    • AI Safety in Finance
    • LLMs in Finance: Practical Examples
    • Financial Report Parsing, Sentiment Analysis

    • Continuation of LLMs in Finance: Practical Examples
    • Extracting Insights: Trends and Analysis, Trading
    • Real World Project
    • Option 1: Parsing Financial Rep

    • Continuation of Real World Project
    • Completion of chosen project

    World-Renowned Machine Learning Faculty

    Our faculty is hand picked to offer you the best learning experience.

    Miquel Noguer Alonso

    Co-Founder & Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

    Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and I+D FinAlpro. He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.
    He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).

    Hanane Dupouy

    Hanane Dupouy is an algorithmic trader in a french bank based in Paris.

    She has a rich and multifaceted career spanning over 13 years in investment banking, where she has held various roles of increasing responsibility. She progressed through positions such as data scientist, business Intelligence analyst, and eventually assumed roles as an algorithmic trader director, showcasing a deep understanding of equity derivatives and trading strategies.
    Hanane Dupouy is a dynamic and innovative professional with a deep passion for artificial intelligence, specializing in Generative AI and Large Language Models. With a solid foundation in engineering from prestigious French institutions, including Arts et Métiers ParisTech, TelecomParisTech and a specialized postgraduate degree in financial techniques from ESSEC Business School, she has carved a niche in the intersection of AI and finance.

    Nicole Königstein

    A distinguished Data Scientist and Quantitative Researcher

    Nicole Königstein is a distinguished Data Scientist and Quantitative Researcher, currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and as Head of AI and Quantitative Research at Quantmate, an innovative FinTech startup focused on alternative data in predictive modeling. Alongside her roles in these organizations, she serves as an AI consultant across diverse industries, leading workshops and guiding companies from the conceptual stages of AI implementation through to final deployment.
    As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a regular speaker at renowned AI and Data Science conferences, where she conducts workshops and educational sessions. In addition, she is an influential voice in the data science community, regularly reviewing books in her field and offering her insights and critiques. Nicole is also the author of the well-received online couse, “Math for Machine Learning.

    David Pacheco Aznar

    Chief of AI Research and Development at RavenRisk AI

    He is developing a custom pipeline for the application of Large Language Models in corporate credit risk in the US market. He is also a faculty member at the AI in Finance Institute, where he has given multiple conferences on Generative Models and Reinforcement Learning, in addition to having published two papers with AIFI on those topics.
    He also developed a custom deep reinforcement learning architecture for equities portfolio management, which he presented at QuantMinds and other conferences. Finally, in the academic realm, he was a teaching fellow at Columbia University on Big Data and Recommender Systems. He is the co-author of two upcoming books: “AI in Finance” for Risk Editorial and “Large Language Models” for Springer Nature, and he studied Computational Mathematics and Data Science at the Autonomous University of Barcelona.

    FAQs

    This course focuses on integrating Large Language Models (LLMs) into the finance sector. It covers the essentials of LLMs, including their architecture, how they operate, and their practical applications in finance. Participants will learn to deploy LLMs for tasks such as financial analysis and customer service, leveraging AI to enhance efficiency and innovation in financial operations.

    The course is designed for finance professionals, developers, and students interested in the intersection of AI and finance. It's ideal for those looking to understand and utilize advanced AI technologies to solve real-world financial problems and improve operational efficiencies.

    Participants will learn:

    • The architecture and operation of Large Language Models.
    • How to deploy LLMs for financial analysis and customer interactions.
    • Techniques for fine-tuning LLMs for domain-specific applications.
    • Methods for implementing retrieval-augmented generation to enhance information processing.
    • How to evaluate the performance of LLMs in financial tasks.

    The course includes a series of practical examples and projects that allow learners to apply LLMs directly to finance-related tasks. These hands-on activities are designed to reinforce the theoretical knowledge gained and provide real-world experience in implementing and optimizing LLMs within the finance sector.

    The skills from this course can be applied in various aspects of finance including banking, investment, risk management, and customer service. With the growing integration of AI in these areas, expertise in LLMs allows for innovative solutions to complex financial challenges.

    The course takes place over 9 weeks for a total of 20 hours of lectures during the course.

    All the lectures are filmed and recordings are available for you on the Student Portal for the duration of the course.

    The current pricing of $1,000 is at a 50% discount compared to global fees of $2,000. As this is a negotiated & heavily discounted price specifically for the Wright community, there are no further discounts or early bird offers.
    However, we do offer volume discounts, so if 2 or more people from your institution wish to take the course please contact us and we will be happy to discuss the pricing.
    In addition to this, if you are interested in taking multiple courses from the following list, then we can offer you specific discounts:

    The live streaming will be available on Cisco Webex, you will be given weekly login access details.

    Students are expected to have a working knowledge of programming as the course delves into algorithmic trading's technical aspects. Familiarity with basic financial concepts and models is also beneficial.

    The primary goal is to equip students with a strong foundation in both the theoretical and practical aspects of algorithmic trading. By the end of the course, students should be capable of developing, implementing, and optimizing their own trading models and strategies.

    By providing both theoretical knowledge and practical skills, the course prepares students for roles in algorithmic trading at banks, proprietary trading firms, and investment management companies. It focuses on real-world applications and innovations in trading, making graduates attractive candidates for a range of positions in finance and technology.

    Yes, the course is designed to bridge the gap between academic learning and real-world trading needs. Students will engage in projects that simulate actual trading situations and implement strategies that can be applied in live trading environments.

    The current pricing of ₹ 2,00,000 is at a 50%+ discount compared to global fees of ₹ 4,10,000 (£ 3,950). As this is a negotiated & heavily discounted price specifically for the Wright community, there are no further discounts or early bird offers.

    However, we do offer volume discounts, so if 2 or more people from your institution wish to take the course please contact us and we will be happy to discuss the pricing.

    In addition to this, if you are interested in taking multiple courses from the following list, then we can offer you specific discounts: