Machine Learning and Reinforcement Learning in Finance Specialization

Start Date: 10/04/2020

Course Type: Specialization Course

Course Link:

About Course

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance. The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) mapping the problem on a general landscape of available ML methods, (2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and (3) successfully implementing a solution, and assessing its performance. The specialization is designed for three categories of students: · Practitioners working at financial institutions such as banks, asset management firms or hedge funds · Individuals interested in applications of ML for personal day trading · Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

Course Syllabus

Guided Tour of Machine Learning in Finance
Fundamentals of Machine Learning in Finance
Reinforcement Learning in Finance
Overview of Advanced Methods of Reinforcement Learning in Finance

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Course Introduction

Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets. Machine Learning and Reinforcement Learning in Finance Specialization In the specialization, we will see how machine learning algorithms work, what properties they have, and how to use them in finance. We will learn about the most common machine learning algorithms and their properties, and how to implement their algorithms. We will also learn how to scale up models to handle real world datasets and how to use linear models to make the most of your data. We will also learn how to tune models to suit the data and the market. You will also learn about the tools and techniques to make your models adapt to changes in the data by using stochastic and continuous optimization. We will also learn about the different types of optimization and their advantages and disadvantages, the use of kernels to optimize models, and the use of random variable optimization in linear models. We will then introduce the various packages associated with each package, its usage, and the main design principles of the model. We will then use the favorite random variable optimization frameworks to make the most of our data, and we will also learn about the linear models that are most suited to our problem space. We will then apply various linearization techniques to make the best use out of our data by using maximum common variance (CV) and average over-fitting. We will then apply different random variable optimization frameworks to the models we design, and we will also learn about the main design principles of the linear models. We will then apply various linearization techniques to the models we design, and we will also learn about the main design principles

Course Tag

Predictive Modelling Financial Engineering Machine Learning Tensorflow Reinforcement Learning

Related Wiki Topic

Article Example
Reinforcement learning In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.
Machine learning Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.
Reinforcement learning Successes of reinforcement learning are listed here.
Reinforcement learning Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take "actions" in an "environment" so as to maximize some notion of cumulative "reward". The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called "approximate dynamic programming". The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
Reinforcement learning The basic reinforcement learning model consists of:
Reinforcement learning Two components make reinforcement learning powerful:
Reinforcement learning Reinforcement learning algorithms such as TD learning are also being investigated as a model for Dopamine-based learning in the brain. In this model, the dopaminergic projections from the substantia nigra to the basal ganglia function as the prediction error. Reinforcement learning has also been used as a part of the model for human skill learning, especially in relation to the interaction between implicit and explicit learning in skill acquisition (the first publication on this application was in 1995-1996, and there have been many follow-up studies).
Reinforcement learning The first two of these problems could be considered planning problems (since some form of the model is available), while the last one could be considered as a genuine learning problem. However, under a reinforcement learning methodology both planning problems would be converted to machine learning problems.
Reinforcement learning There is also a growing interest in real life applications of reinforcement learning.
Reinforcement learning A reinforcement learning agent interacts with its environment in discrete time steps.
Reinforcement learning Multiagent or Distributed Reinforcement Learning is also a topic of interest in current research.
Reinforcement learning Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs.
Active learning (machine learning) Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.
Reinforcement learning There are multiple applications of reinforcement learning to generate models and train them to play video games, such as Atari games. In these models, reinforcement learning finds the actions with the best reward at each play. This method is a widely used method in combination with deep neural networks to teach computers to play Atari video games.
Reinforcement learning Thanks to these two key components, reinforcement learning can be used in large environments in any of the following situations:
Machine learning Reinforcement learning is concerned with how an "agent" ought to take "actions" in an "environment" so as to maximize some notion of long-term "reward". Reinforcement learning algorithms attempt to find a "policy" that maps "states" of the world to the actions the agent ought to take in those states. Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.
Reinforcement learning Most reinforcement learning papers are published at the major machine learning and AI conferences (ICML, NIPS, AAAI, IJCAI, UAI, AI and Statistics) and journals (JAIR, JMLR, Machine learning journal, IEEE T-CIAIG). Some theory papers are published at COLT and ALT. However, many papers appear in robotics conferences (IROS, ICRA) and the "agent" conference AAMAS. Operations researchers publish their papers at the INFORMS conference and, for example, in the Operation Research, and the Mathematics of Operations Research journals. Control researchers publish their papers at the CDC and ACC conferences, or, e.g., in the journals IEEE Transactions on Automatic Control, or Automatica, although applied works tend to be published in more specialized journals. The Winter Simulation Conference also publishes many relevant papers. Other than this, papers also published in the major conferences of the neural networks, fuzzy, and evolutionary computation communities. The annual IEEE symposium titled Approximate Dynamic Programming and Reinforcement Learning (ADPRL) and the biannual European Workshop on Reinforcement Learning (EWRL) are two regularly held meetings where RL researchers meet.
Machine learning Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.
Reinforcement learning In reinforcement learning methods the expectations are approximated by averaging over samples and one uses function approximation techniques to cope with the need to represent value functions over large state-action spaces.
Adversarial machine learning Adversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe adoption of machine learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition.