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Please note that this course will reference Python and cloud-based technologies, but Python/technical experience is not required to attend.
Data science, machine learning and artificial intelligence (AI) are becoming more widely used in the investment industry to augment traditional investment decision making. This one day, hands on course brings clarity to how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI. The curriculum will not teach attendees to write code or run experiments in Python, it will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
ML and AI: An intuitive Introduction
- Machine Learning vs Statistics: How has the world changed?
- Key drivers influencing the adoption of Machine Learning and AI
- Big Data, Hardware, Fintech, AI, Alternative Data
- Key applications
- Credit risk, Personalization, Predicting risk, Portfolio optimization and selection
- A deep dive on Machine Learning and AI methods
- Supervised Learning (Regression, Random Forest and Neural Network)
- Unsupervised Learning (K-means)
- Deep Learning (Keras)
- Reinforcement Learning
- Performance evaluation and tuning
Case study 1: Predicting interest rates and credit risk using Alternative data sets.
- Exploring and Visualizing large datasets:
- Building a Credit risk model using Regression, Random Forest and Neural Network Algorithms
- Tuning your model
- Automatic Machine Learning
Case study 2: Analyzing Earning calls using text analytics
- Making sense of Text and Natural Language Processing- The Decalogue
- Sentiment Analysis: How to interpret sentiments and use it in stock selection?
- Comparing Amazon, Google, Microsoft APIs for Sentiment Analysis
- Key issues in adopting AI and Machine learning into investment workflows
- How will Machine Learning and AI change the investment industry
- Frontier topics
- Anomaly detection
- Reinforcement learning
- Risk in Machine Learning and AI
- Model governance, Interpretability and Model Management