Master the fundamentals of machine learning applications in finance, from crowdfunding and P2P lending to robo-advising and quantitative investments.
Master the fundamentals of machine learning applications in finance, from crowdfunding and P2P lending to robo-advising and quantitative investments.
This comprehensive course explores the intersection of machine learning and financial technology, providing professionals with essential knowledge of AI applications in finance. The curriculum covers three main areas: crowdfunding markets including equity crowdfunding and P2P lending, artificial intelligence fundamentals with practical applications in finance using Python, and modern investment technologies like robo-advising and social platforms. Students will gain practical skills in implementing machine learning models for financial applications, understanding predictive modeling, and evaluating AI-driven financial products.
Instructors:
English
English
What you'll learn
Master machine learning algorithms and their financial applications
Understand crowdfunding, robo-advising, and social investing platforms
Develop skills in using Python for financial modeling
Learn to evaluate AI-driven financial products
Gain expertise in quantitative investment strategies
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
The course provides a comprehensive exploration of machine learning applications in finance through three key modules. First, it examines the crowdfunding market, covering equity crowdfunding and P2P lending. The second module focuses on artificial intelligence fundamentals, including machine learning tools, predictive models, and practical applications using Python. The final module covers modern investment technologies like robo-advising and social platform finance. Throughout the course, students learn both theoretical concepts and practical applications.
P2P Crowdfunding
Module 1
Overview of Artificial Intelligence
Module 2
Advanced Machine Learning
Module 3
Modern Investment Technologies
Module 4
Instructor

4 Courses
Leading Expert in Corporate Finance and Financial Technology
Dr. Cesare Fracassi serves as Associate Professor of Finance and Director of the Blockchain Initiative at the University of Texas at Austin's McCombs School of Business, where he combines expertise in corporate finance with innovative research in financial technology. After completing his BS and MS in Electrical Engineering from Politecnico di Milano and PhD in Finance from UCLA Anderson, he has established himself as a highly cited researcher with over 3,800 citations. His work spans multiple areas including corporate governance, credit rating agencies, and the impact of social networks on financial decision-making. As Director of the Fintech Research Lab and through his groundbreaking research on data autonomy and corporate culture, he continues to advance understanding of how social connections and technology influence financial policies. His expertise particularly shines in analyzing how social networks affect corporate decision-making, demonstrated through his influential work on external networking and internal firm governance. Through his teaching of fintech, corporate finance, and valuation across multiple programs and his visiting professorship at Wharton, he helps bridge theoretical research with practical applications while maintaining active investigation of emerging financial technologies. Before entering academia, he gained practical experience as a strategic management consultant at Booz Allen and Roland Berger in Italy and as an intern at the United Nations in New York.
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