Hello, and welcome to chartered AI investment analyst.
This is the course overview where we'll talk a little bit
about the course objective.
Uh, how the course is structured and what you can expect.
To learn.
So I'm your lecturer, Michael Wayne.
And I'll be taking you through this course.
Uh, so the chartered AI investment analyst.
Uh, six, two.
Hope students who are seeking to merge powerful machine learning and
artificial intelligence methods.
With the existing domain knowledge and investing.
To help you to make robust our data driven investment decisions.
So this will done will be done across 10 modules while you'll navigate
from understanding the very basics.
And foundations of, uh, both AI and investment strategies.
No DAS, you already have some expertise in that particular field.
But we will, uh, we will, um, refresh our memories on some core concepts,
especially in respects to how they relate to using, um, investment analysis with AI.
Uh, and we'll also move on to cover more advanced machine
learning and AI models for.
Investing.
The curriculum will help you with.
Core data science skills.
Uh, including Python programming, that is the programming language with which
in, um, in a lot of the methods that we learn about in this particular course.
We'll also learn how to work with financial data, how to.
Wrap it in a way where it's ready for use with AI machine learning methods.
And, uh, we'll also.
Uh, learn to work with.
Next Stata or natural language.
Processing in the same context.
A little bit about me.
I'm a CFA charter holder.
I also run my own consultancy in AI and investment analytics called widespread.
Um, the head of data science in Australia at a FinTech company.
And I have over 13 years experience across investment and data science consulting
teaching, and the broader BARDA landscape.
So how the course will work is first we'll start with a introduction to
AI and a refresh on core investment concepts that are relevant to us.
Then we'll move on to a, uh, introduction to Python programming with all the core
concepts needed to help us to program up.
Some of these are AI methods.
And then that will form the perfect segue into an introduction to
machine learning, which the is the broader field that encompasses AI.
Once we're co.
Stubble with some of the foundation concepts, we'll move on to
advanced machine learning and we'll discuss more advanced models.
Uh, as well as out to avoid over-fitting how to evaluate models
and additional considerations for applying machine learning models in
the context of investment analysis.
Next we'll move on to another specific subset of machine learning.
Called deep learning.
Deep learning is, uh, the.
I have models that is at the forefront of a lot of AI
advancements that have happened.
And the last two or three years.
So it is very relevant.
So we'll discuss.
Uh, at a base level, how works.
Um, give you the core skills to understand and build deep learning models.
And then in module six.
We'll move on to a very specialized type of deep Mo.
Deep learning models, which are the.
Uh, RNN, LSTM and transformers, which especially design models to
deal with sequential data such as financial time series and tech startup.
And speaking about tech Stata will also be exposed to a lot of, um, uh, financial
tech starter throughout this course.
So it's only fitting that we also have a, um, Introduction to natural
language processing, which is the subfield of AI and machine learning.
That looks at, um, Uh, how computers deal with an understand tech startup.
Um, how we can apply it to our investment analysis.
In module eight we'll specifically look at techniques such as sentiment analysis.
Uh, topic mining.
Uh, as well as a range of other techniques, which we can apply
directly to financial texts.
In module nine.
We'll cover large language models and of course low our discussion
now, large language models be complete without chat GPT.
We'll talk about how to set up chat GPT and can actually get the most
out of it with effective prompting.
And then we'll apply some examples of where we can use that to augment.
Our, uh, investment analysis, introducing the, uh, the Atlas copilot model.
In module 10, finally.
We'll uh, Regress the discussion around large language models with how to work.
With large language models and the advanced metals.
Uh, that will be followed with a discussion.
On.
Model.
Explainability and why it's particularly important in the context
of using AI in investment analysis.
models.
And finally, uh, I'll leave you with a framework for applying
AI to in assessment analysis.
That's it for the overview.
I hope you found that informative and I hope to see you soon in the lessons.
Thank you.