Can I become a quant with a CS degree?

While an undergraduate degree in mathematics, theoretical physics, computer science or EEE are most appropriate for quant roles, there are also other degrees that can lead to a top quant role, usually via a postgraduate route.

Is machine learning used in quantitative finance?

Over the past ten years or so, interest in machine learning (ML) in its various forms has risen dramatically. In quant finance, ML plays a role in valuation, asset allocation, risk management, and compliance.

Is CS a quantitative field?

Your academic background is in a quantitative field such as Computer Science, Computational Linguistics, Mathematics/Statistics, Engineering, Economics or Physics.

Do quants traders use machine learning?

Machine Learning techniques are statistically driven and have been used by quants for a long time. Machine Learning is most effective at improving parts of the trade life-cycle process, such as data processing & modelling, forecasting & signal research, risk management and execution.

How do quants use machine learning?

A quant will tend to use a data sample, an out of the box model from a software package like R, and fit it in a standard way. A machine learning person might train it on a larger dataset over a larger parameter space with a loss function and optimization algorithm tailored to the specific problem.

Are all machine learning problems explicit optimization problems?

All machine learning problems can be cast as explicit optimization problems. Machine learning is the set of optimization problems where the majority of constraints come from measured datapoints, as opposed to prior domain knowledge. [0] Fun fact: Marcus Hutter solved Artificial General Intelligence a decade ago.

What is the difference between machine learning and optimization in AI?

, AI researcher. The difference is very slim between machine learning (ML) and optimization theory. In ML the idea is to learn a function that minimizes an error or one that maximizes reward over punishment. Yes a lot of learning can be seen as optimization. In fact learning is an optimization problem.

How is linear algebra related to machine learning?

Although linear algebra is integral to the field of machine learning, the tight relationship is often left unexplained or explained using abstract concepts such as vector spaces or specific matrix operations.

How do I use optimization (steel) for machine learning?

Technically you don’t need to use optimization (steel) for machine learning (making saws), although most of the time you’ll find that it’s used somewhere in the ML algorithm you’re looking at. Also, optimization (steel) can be used for a variety of things that are not machine learning.