Machine Learning and Reinforcement Learning in Finance Specialization

About Course

Machine Learning and Reinforcement Learning in Finance Specialization, 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 essentially in ML where all examples, home assignments and course projects deal with various problems in Finance (such as stock trading, asset management, and banking applications), and the choice of topics is respectively driven by a focus on ML methods that are used by practitioners in Finance. The specialization is meant to prepare the students to work on complex machine learning projects in finance that often require both a broad understanding of the whole field of ML, and understanding of appropriateness of different methods available in a particular sub-field of ML (for example, Unsupervised Learning) for addressing practical problems they might encounter in their work.

Course Content

01. Guided Tour of Machine Learning in Finance

004 01_welcome-note.mp4

00:00
008 02_specialization-objectives.mp4

00:00
012 03_specialization-prerequisites.mp4

00:00
016 01_artificial-intelligence-and-machine-learning-part-i.mp4

00:00
020 02_artificial-intelligence-and-machine-learning-part-ii.mp4

00:00
024 01_machine-learning-as-a-foundation-of-artificial-intelligence-part-i.mp4

00:00
028 02_machine-learning-as-a-foundation-of-artificial-intelligence-part-ii.mp4

00:00
032 03_machine-learning-as-a-foundation-of-artificial-intelligence-part-iii.mp4

00:00
036 01_machine-learning-in-finance-vs-machine-learning-in-tech-part-i.mp4

00:00
040 02_machine-learning-in-finance-vs-machine-learning-in-tech-part-ii.mp4

00:00
044 03_machine-learning-in-finance-vs-machine-learning-in-tech-part-iii.mp4

00:00
045 01_the-business-of-artificial-intelligence_instructions.html

00:00
046 02_how-ai-and-automation-will-shape-finance-in-the-future_instructions.html

00:00
047 03_a-geron-hands-on-machine-learning-with-scikit-learn-and-tensorflow-chapter-1_instructions.html

00:00
053 01_generalization-and-a-bias-variance-tradeoff.mp4

00:00
058 02_the-no-free-lunch-theorem.mp4

00:00
063 03_overfitting-and-model-capacity.mp4

00:00
068 04_linear-regression.mp4

00:00
072 05_regularization-validation-set-and-hyper-parameters.mp4

00:00
076 06_overview-of-the-supervised-machine-learning-in-finance.mp4

00:00
077 01_i-goodfellow-y-bengio-a-courville-deep-learning-chapters-4-5-5-1-5-2-5-3-5-4_instructions.html

00:00
078 02_leo-breiman-statistical-modeling-the-two-cultures_instructions.html

00:00
079 01_module-2-quiz_exam.html

00:00
080 02_jupyter-notebook-faq_instructions.html

00:00
081 03_euclidean-distance-calculation_instructions.html

00:00
086 01_dataflow-and-tensorflow.mp4

00:00
090 02_a-first-demo-of-tensorflow.mp4

00:00
094 03_linear-regression-in-tensorflow.mp4

00:00
099 04_neural-networks.mp4

00:00
104 05_gradient-descent-optimization.mp4

00:00
109 06_gradient-descent-for-neural-networks.mp4

00:00
112 07_stochastic-gradient-descent.mp4

00:00
113 01_a-geron-hands-on-ml-chapter-9-chapter-4-gradient-descent_instructions.html

00:00
114 02_e-fama-and-k-french-size-and-book-to-market-factors-in-earnings-and-returns_instructions.html

00:00
115 03_j-piotroski-value-investing-the-use-of-historical-financial-statement_instructions.html

00:00
116 01_module-3-quiz_exam.html

00:00
117 02_jupyter-notebook-faq_instructions.html

00:00
118 03_linear-regression_instructions.html

00:00
123 01_regression-and-equity-analysis.mp4

00:00
128 02_fundamental-analysis.mp4

00:00
133 01_machine-learning-as-model-estimation.mp4

00:00
138 02_maximum-likelihood-estimation.mp4

00:00
143 03_probabilistic-classification-models.mp4

00:00
148 04_logistic-regression-for-modeling-bank-failures-part-i.mp4

00:00
153 05_logistic-regression-for-modeling-bank-failures-part-ii.mp4

00:00
158 06_logistic-regression-for-modeling-bank-failures-part-iii.mp4

00:00
162 07_supervised-learning-conclusion.mp4

00:00
163 01_c-bishop-pattern-recognition-and-machine-learning-chapters-4-1-4-2-4-3_instructions.html

00:00
164 02_a-geron-hands-on-ml-chapters-3-chapter-4-logistic-regression_instructions.html

00:00
165 01_module-4-quiz_exam.html

00:00
166 02_jupyter-notebook-faq_instructions.html

00:00
167 03_tobit-regression_instructions.html

00:00
168 01_jupyter-notebook-faq_instructions.html

00:00
169 02_course-project_instructions.html

00:00
170 01__resources.html

00:00

02. Fundamentals of Machine Learning in Finance

006 02_introduction-to-fundamentals-of-machine-learning-in-finance.mp4

00:00
010 01_support-vector-machines-part-1.mp4

00:00
014 02_support-vector-machines-part-2.mp4

00:00
018 03_svm-the-kernel-trick.mp4

00:00
022 04_example-svm-for-prediction-of-credit-spreads.mp4

00:00
023 05_a-smola-and-b-scholkopf-a-tutorial-on-support-vector-regression-statistics-and_instructions.html

00:00
027 01_tree-methods-cart-trees.mp4

00:00
031 02_tree-methods-random-forests.mp4

00:00
035 03_tree-methods-boosting.mp4

00:00
036 04_a-geron-hands-on-machine-learning-with-scikit-learn-and-tensorflow-chapters-6-7_instructions.html

00:00
037 05_k-murphy-machine-learning-a-probabilistic-perspective-mit-press-2009-chapter-16_instructions.html

00:00
038 01_jupyter-notebook-faq_instructions.html

00:00
039 02_random-forests-and-decision-trees_instructions.html

00:00
043 01_core-concepts-of-ul.mp4

00:00
051 02_pca-for-stock-returns-part-2.mp4

00:00
052 03_c-bishop-pattern-recognition-and-machine-learning-chapter-12-1_instructions.html

00:00
056 01_dimension-reduction-with-pca.mp4

00:00
060 02_dimension-reduction-with-tsne.mp4

00:00
064 03_dimension-reduction-with-autoencoders.mp4

00:00
065 04_a-geron-hands-on-ml-chapters-8-15_instructions.html

00:00
066 01_jupyter-notebook-faq_instructions.html

00:00
067 02_eigen-portfolio-construction-via-pca_instructions.html

00:00
071 01_ul-clustering-algorithms.mp4

00:00
075 02_ul-k-clustering.mp4

00:00
079 03_ul-k-means-neural-algorithm.mp4

00:00
083 04_ul-hierarchical-clustering-algorithms.mp4

00:00
087 05_ul-clustering-and-estimation-of-equity-correlation-matrix.mp4

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091 06_ul-minimum-spanning-trees-kruskal-algorithm.mp4

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095 07_ul-probabilistic-clustering.mp4

00:00
096 08_c-bishop-pattern-recognition-and-machine-learning-clustering-and-em-chapter-9_instructions.html

00:00
097 09_g-bonanno-et-al-networks-of-equities-in-financial-markets-the-european-physical_instructions.html

00:00
098 01_jupyter-notebook-faq_instructions.html

00:00
099 02_data-visualization-with-t-sne_instructions.html

00:00
103 01_sm-latent-variables.mp4

00:00
107 02_sequence-modeling.mp4

00:00
111 03_sm-latent-variables-for-sequences.mp4

00:00
115 04_sm-state-space-models.mp4

00:00
119 05_sm-hidden-markov-models.mp4

00:00
123 06_neural-architecture-for-sequential-data.mp4

00:00
124 07_c-bishop-pattern-recognition-and-machine-learning-chapter-13_instructions.html

00:00
128 01_rl-introduction.mp4

00:00
132 02_rl-core-ideas.mp4

00:00
136 03_markov-decision-process-and-rl.mp4

00:00
140 04_rl-bellman-equation.mp4

00:00
144 05_rl-and-inverse-reinforcement-learning.mp4

00:00
145 06_s-marsland-machine-learning-an-algorithmic-perspective-chapman-hall-2009-chapter_instructions.html

00:00
146 01_jupyter-notebook-faq_instructions.html

00:00
147 02_absorption-ratio-via-pca_instructions.html

00:00
148 01__resources.html

00:00

03. Reinforcement Learning in Finance

006 02_prerequisites.mp4

00:00
009 03_welcome-to-the-course.mp4

00:00
013 01_introduction-to-markov-decision-processes-and-reinforcement-learning-in-finance.mp4

00:00
017 02_mdp-and-rl-decision-policies.mp4

00:00
021 03_mdp-rl-value-function-and-bellman-equation.mp4

00:00
025 04_mdp-rl-value-iteration-and-policy-iteration.mp4

00:00
029 05_mdp-rl-action-value-function.mp4

00:00
033 01_options-and-option-pricing.mp4

00:00
037 02_black-scholes-merton-bsm-model.mp4

00:00
041 03_bsm-model-and-risk.mp4

00:00
045 04_discrete-time-bsm-model.mp4

00:00
049 05_discrete-time-bsm-hedging-and-pricing.mp4

00:00
053 06_discrete-time-bsm-bs-limit.mp4

00:00
054 01_jupyter-notebook-faq_instructions.html

00:00
055 02_discrete-time-black-scholes-model_instructions.html

00:00
056 03_hedged-monte-carlo-low-variance-derivative-pricing-with-objective-probabilities_instructions.html

00:00
060 01_mdp-formulation.mp4

00:00
064 02_action-value-function.mp4

00:00
068 03_optimal-action-from-q-function.mp4

00:00
071 04_backward-recursion-for-q-star.mp4

00:00
075 01_basis-functions.mp4

00:00
078 02_optimal-hedge-with-monte-carlo.mp4

00:00
081 03_optimal-q-function-with-monte-carlo.mp4

00:00
082 01_jupyter-notebook-faq_instructions.html

00:00
083 02_qlbs-model-implementation_instructions.html

00:00
084 03_qlbs-q-learner-in-the-black-scholes-merton-worlds_instructions.html

00:00
088 01_week-introduction.mp4

00:00
091 02_batch-reinforcement-learning.mp4

00:00
094 03_stochastic-approximations.mp4

00:00
097 04_q-learning.mp4

00:00
100 05_fitted-q-iteration.mp4

00:00
103 06_fitted-q-iteration-the-ps-basis.mp4

00:00
106 07_fitted-q-iteration-at-work.mp4

00:00
109 08_rl-solution-discussion-and-examples.mp4

00:00
110 01_jupyter-notebook-faq_instructions.html

00:00
111 02_fitted-q-iteration_instructions.html

00:00
112 03_qlbs-q-learner-in-the-black-scholes-merton-worlds-and-the-qlbs-learner-goes_instructions.html

00:00
113 04_course-project-reading-global-portfolio-optimization_instructions.html

00:00
117 01_week-welcome-video.mp4

00:00
120 02_introduction-to-rl-for-trading.mp4

00:00
123 03_portfolio-model.mp4

00:00
126 04_one-period-rewards.mp4

00:00
129 05_forward-and-inverse-optimisation.mp4

00:00
132 06_reinforcement-learning-for-portfolios.mp4

00:00
135 07_entropy-regularized-rl.mp4

00:00
138 08_rl-equations.mp4

00:00
141 09_rl-and-inverse-reinforcement-learning-solutions.mp4

00:00
144 10_course-summary.mp4

00:00
145 01_jupyter-notebook-faq_instructions.html

00:00
146 02_irl-market-model-calibration_peer_assignment_instructions.html

00:00
148 03_multi-period-trading-via-convex-optimization_instructions.html

00:00
003 01_introduction-to-the-specialization.mp4

00:00
links.txt

00:00

04. Overview of Advanced Methods of Reinforcement Learning in Finance

003 01_welcome-to-specialization.mp4

00:00
006 02_specialization-prerequisites.mp4

00:00
009 03_interview-with-rossen-roussev.mp4

00:00
012 01_reinforcement-learning-and-ptolemys-epicycles.mp4

00:00
015 02_pdes-in-physics-and-finance.mp4

00:00
018 03_competitive-market-equilibrium-models-in-finance.mp4

00:00
021 04_i-certainly-hope-you-are-wrong-herr-professor.mp4

00:00
024 05_risk-as-a-science-of-fluctuation.mp4

00:00
027 06_markets-and-the-heat-death-of-the-universe.mp4

00:00
031 01_option-trading-and-rl.mp4

00:00
034 02_liquidity.mp4

00:00
037 03_modeling-market-frictions.mp4

00:00
040 04_modeling-feedback-frictions.mp4

00:00
045 01_from-portfolio-optimization-to-market-model.mp4

00:00
048 02_invisible-hand.mp4

00:00
051 03_gbm-and-its-problems.mp4

00:00
054 04_the-gbm-model-an-unbounded-growth-without-defaults.mp4

00:00
057 05_dynamics-with-saturation-the-verhulst-model.mp4

00:00
060 06_the-singularity-is-near.mp4

00:00
063 07_what-are-defaults.mp4

00:00
066 08_quantum-equilibrium-disequilibrium.mp4

00:00
071 01_welcome.mp4

00:00
074 02_market-dynamics-and-irl.mp4

00:00
077 03_diffusion-in-a-potential-the-langevin-equation.mp4

00:00
080 04_classical-dynamics.mp4

00:00
083 05_potential-minima-and-newtons-law.mp4

00:00
086 06_classical-dynamics-the-lagrangian-and-the-hamiltonian.mp4

00:00
089 07_langevin-equation-and-fokker-planck-equations.mp4

00:00
092 08_the-fokker-planck-equation-and-quantum-mechanics.mp4

00:00
097 01_welcome.mp4

00:00
100 02_electronic-markets-and-lob.mp4

00:00
103 03_trades-quotes-and-order-flow.mp4

00:00
106 04_limit-order-book.mp4

00:00
109 05_lob-modeling.mp4

00:00
112 06_lob-statistical-modeling.mp4

00:00
115 07_lob-modeling-with-ml-and-rl.mp4

00:00
118 08_other-applications-of-rl.mp4

00:00
121 09_the-value-of-universatility.mp4

00:00
123 01_final-project_peer_assignment_instructions.html

00:00
links.txt

00:00

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About Course

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Course Content

01. Guided Tour of Machine Learning in Finance

004 01_welcome-note.mp4

008 02_specialization-objectives.mp4

012 03_specialization-prerequisites.mp4

016 01_artificial-intelligence-and-machine-learning-part-i.mp4

020 02_artificial-intelligence-and-machine-learning-part-ii.mp4

024 01_machine-learning-as-a-foundation-of-artificial-intelligence-part-i.mp4

028 02_machine-learning-as-a-foundation-of-artificial-intelligence-part-ii.mp4

032 03_machine-learning-as-a-foundation-of-artificial-intelligence-part-iii.mp4

036 01_machine-learning-in-finance-vs-machine-learning-in-tech-part-i.mp4

040 02_machine-learning-in-finance-vs-machine-learning-in-tech-part-ii.mp4

044 03_machine-learning-in-finance-vs-machine-learning-in-tech-part-iii.mp4

045 01_the-business-of-artificial-intelligence_instructions.html

046 02_how-ai-and-automation-will-shape-finance-in-the-future_instructions.html

047 03_a-geron-hands-on-machine-learning-with-scikit-learn-and-tensorflow-chapter-1_instructions.html

053 01_generalization-and-a-bias-variance-tradeoff.mp4

058 02_the-no-free-lunch-theorem.mp4

063 03_overfitting-and-model-capacity.mp4

068 04_linear-regression.mp4

072 05_regularization-validation-set-and-hyper-parameters.mp4

076 06_overview-of-the-supervised-machine-learning-in-finance.mp4

077 01_i-goodfellow-y-bengio-a-courville-deep-learning-chapters-4-5-5-1-5-2-5-3-5-4_instructions.html

078 02_leo-breiman-statistical-modeling-the-two-cultures_instructions.html

079 01_module-2-quiz_exam.html

080 02_jupyter-notebook-faq_instructions.html

081 03_euclidean-distance-calculation_instructions.html

086 01_dataflow-and-tensorflow.mp4

090 02_a-first-demo-of-tensorflow.mp4

094 03_linear-regression-in-tensorflow.mp4

099 04_neural-networks.mp4

104 05_gradient-descent-optimization.mp4

109 06_gradient-descent-for-neural-networks.mp4

112 07_stochastic-gradient-descent.mp4

113 01_a-geron-hands-on-ml-chapter-9-chapter-4-gradient-descent_instructions.html

114 02_e-fama-and-k-french-size-and-book-to-market-factors-in-earnings-and-returns_instructions.html

115 03_j-piotroski-value-investing-the-use-of-historical-financial-statement_instructions.html

116 01_module-3-quiz_exam.html

117 02_jupyter-notebook-faq_instructions.html

118 03_linear-regression_instructions.html

123 01_regression-and-equity-analysis.mp4

128 02_fundamental-analysis.mp4

133 01_machine-learning-as-model-estimation.mp4

138 02_maximum-likelihood-estimation.mp4

143 03_probabilistic-classification-models.mp4

148 04_logistic-regression-for-modeling-bank-failures-part-i.mp4

153 05_logistic-regression-for-modeling-bank-failures-part-ii.mp4

158 06_logistic-regression-for-modeling-bank-failures-part-iii.mp4

162 07_supervised-learning-conclusion.mp4

163 01_c-bishop-pattern-recognition-and-machine-learning-chapters-4-1-4-2-4-3_instructions.html

164 02_a-geron-hands-on-ml-chapters-3-chapter-4-logistic-regression_instructions.html

165 01_module-4-quiz_exam.html

166 02_jupyter-notebook-faq_instructions.html

167 03_tobit-regression_instructions.html

168 01_jupyter-notebook-faq_instructions.html

169 02_course-project_instructions.html

170 01__resources.html

02. Fundamentals of Machine Learning in Finance

006 02_introduction-to-fundamentals-of-machine-learning-in-finance.mp4

010 01_support-vector-machines-part-1.mp4

014 02_support-vector-machines-part-2.mp4

018 03_svm-the-kernel-trick.mp4

022 04_example-svm-for-prediction-of-credit-spreads.mp4

023 05_a-smola-and-b-scholkopf-a-tutorial-on-support-vector-regression-statistics-and_instructions.html

027 01_tree-methods-cart-trees.mp4

031 02_tree-methods-random-forests.mp4

035 03_tree-methods-boosting.mp4

036 04_a-geron-hands-on-machine-learning-with-scikit-learn-and-tensorflow-chapters-6-7_instructions.html

037 05_k-murphy-machine-learning-a-probabilistic-perspective-mit-press-2009-chapter-16_instructions.html

038 01_jupyter-notebook-faq_instructions.html

039 02_random-forests-and-decision-trees_instructions.html

043 01_core-concepts-of-ul.mp4

051 02_pca-for-stock-returns-part-2.mp4

052 03_c-bishop-pattern-recognition-and-machine-learning-chapter-12-1_instructions.html

056 01_dimension-reduction-with-pca.mp4

060 02_dimension-reduction-with-tsne.mp4

064 03_dimension-reduction-with-autoencoders.mp4

065 04_a-geron-hands-on-ml-chapters-8-15_instructions.html