One of the key players behind the staggering speed of advancement in machine learning and AI are foundation models. But what is a foundation model, really? In this talk, I’ll explain what exactly defines a foundation model, what they’re capable of doing, and review some well-known examples of foundation models in the real world.

An information session introducing the 5-day in-person Large Language Models Bootcamp on 2024-08-08. Topics include bootcamp structure and agenda, core topics covered, hands-on projects, and real-world applications. Meet the expert trainers.

In this session, I will share how I have approached learning the foundations of Machine Learning to transition into a role as an ML engineer, despite my non-traditional background. How I use chatgpt and other generative AI tools to assist both at work and on my side projects. The pros and cons I see of using these tools and why I think it's important we take notice of these technologies.

In this talk, we will share insights into the use of ML techniques, such as object detection and classification, to improve video meetings on our Cisco devices. We'll discuss our wide range of ML models and their respective use cases. The session will include a focused examination of our head detection model, detailing the fundamental principles and demonstrating the specific functionalities it facilitates to refine the video meeting experience.

This project aims to develop an AI-powered system that predicts the most cost-effective locations for users to book flights, ensuring they can access the cheapest possible prices. The project aims to generate a rich dataset generated through various web scraping techniques, querying flight data and prices from various locations. Utilizing a machine learning model, the system analyzes this comprehensive dataset to suggest optimal booking locations and predict potential savings for a user given a query.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

This talk explores Java code structure analysis using Graphs. It provides an introduction to Graphs and underscores their significance in both Graph Analysis and the field of Graph Data Science. The journey begins with exploring queries to analyze code dependencies and progresses to the application of graph algorithms for tasks such as community detection, centrality, and similarity. Additionally, the talk provides an introduction to node embeddings for machine learning. By the end of this presentation, software professionals will be well-equipped to extract valuable insights from Java code bases effectively.

Outline: Stock trading; Starting with trading; Machine learning for stock predictions.

Learn about the latest trends and developments in AI and ML from leading experts in the field.

Join dotData Sr. Data Scientist Sharada Narayanan as she dives into the strengths, uses, and limitations of popular time-series forecasting techniques like ARIMA and Prophet.

Sharada will walk through real-world examples, share code snippets, and explore how ARIMA Prophet compare when building models using Feature Engineering techniques and advanced machine learning algorithms. Dive into the insights of each method and see how programmatic feature engineering and machine learning can supercharge your time series analysis.

Testing Benjamin Graham's value investing system in stock market using selected value investing features of data input from the quarterly financials of companies. Value investing is interested in the real calculated value of each company according to its financials and has no interest in the price fluctuations of the market. Hence a portfolio of stocks are selected that are cheap and long term return are collected until the stock price reachs saturation. Project is going to feed in these features (collected from historical stock data) into a model and hope to come up with a better weighted system with the help of data science and machine learning practices.

Part of a 40-minute webinar on AI and cybersecurity, presented by Dr James Stanger. This talk covers the layers and foundations of AI/ML in cybersecurity.

Part of a 40-minute webinar on AI and cybersecurity, presented by Dr James Stanger. This talk discusses IT industry outlook for 2023 and how AI/ML will shape the future.

Part of a 40-minute webinar on AI and cybersecurity, presented by Dr James Stanger. This talk explores current real-world uses of AI/ML and future possibilities in the industry.

Part of a 40-minute webinar on AI and cybersecurity, presented by Dr James Stanger. This talk covers the Big Four foundations of emerging technology as they relate to AI/ML and cybersecurity.