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Certificate Program in Machine Learning For Finance (CPMLF)
Build your career in Machine Learning For Finance
Live Online Instructor-led Weekend Program
- English
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Indian Institute of Quantitative Finance
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Quick Facts
- Program Duration
- Program Schedule
- Program Timing
- Program Start Date
CPMLF Program | Machine Learning For Finance Course Highlights
- Comprehensive & Exhaustive Modules
- Weekly Classroom Style Live Sessions
- Python Practice Labs - Python Machine Learning Libraries
- Front-to-Back ML Applications Development
- Financial Dataset Domain Use Case Studies
- Special Focus on Interpretability & Explainibility of Black Box ML Algorithms & Models
- Tech Stack - Machine Learning Pipelines & Development
- BFSI Industry Focused - Capstone Project
About Certificate Program in Machine Learning for Finance (CPMLF)
CPMLF - BFSI Career Roles
- AI & ML Researcher
- AI & ML Model Validator
- Explainable AI & ML Expert
- AI & ML Algo Designer
- AI & ML Developer
- AI & ML Product Owner
CPMLF - BFSI Core Competencies
- Statistical Skills - Descriptive & Inferential Statistics
- Probability Skills - Random Events, Experiments & Expectations
- Mathematical Skills - Linear algebra, matrix operations, differential & integral calculus
- Model development & validation skills
- AI & ML methodology & Techniques
- AI & ML model performance evaluation & validation - Programming Skills - Model building & deployment in PYTHON
CPMLF - Key Learning Outcomes
- Supervised learning techniques
- Unsupervised learning techniques
- Deep learning techniques
- Reinforcement learning techniques
- Large natural language learning
- Model optimization techniques
- Model performance evaluation
- AI & ML expandability
- Model validation testing
- BFSI Data Scientist
CPMLF Course Outline
Module 1 - Applied Mathematics for ML
2 Weeks
- Statistical & Probability Theory & Applications
- Linear Algebra & Matrix Vectorized Operations
- Differential Calculus
- Integral Calculus
- Functional Estimation & Global Minima/Maxima Based Optimization
- Mathematical Modelling & Formulation
Module 2 – ML Supervised Learning Methods
8 Weeks
- Statistical & ML Driven Regression - OLS, MLE, LASSO, RIDGE, Elastic-Net
- Statistical & ML Driven Classification - Linear Classifiers (Logistic Logit, Probit Regression), Bagging (CART Decision Tree, Random forest), Boosting (Ada-Boost, XG-Boost), Support Vector Machines (SVM)
- ML Model Hyper-Parameter Tuning & K-Fold Cross Validation
- Machine Learning Model Optimization, Performance Evaluation & Model Explainibility
- ML Quantitative Validation Tests
Module 3 - ML Un-Supervised Learning Methods
2 Weeks
- Unsupervised & Semi-Supervised Learning
- Statistical & ML Driven Clustering & Association - Hierarchal Clustering & Discriminant Analysis, K-Means Clustering, K-Nearest Neighbors (KNN)
Module 4 - ML Deep Learning Methods
5 Weeks
- Deep Learning - Neural Network (DNN) Intro
- Multi Layer Perceptron (MLP)
- Artificial Neural Network (ANN)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Network (RNN)
- RNN-Long Short Term Memory (LSTM)
- RNN - Gated Recurrent Unit (GRU)
- Deep Reinforcement Learning (DRL)
Module 5 - ML Natural Language Models (NLP)
3 Weeks
- Unstructured Data Sets & Transformations
- ML Driven Textual & Speech Processing
- ML Driven Document Classification
- ML Driven Image Classification
- ML Driven Chat-bots
Module 6 - Capstone Project
1 Month
- BFSI application project supervised by a BFSI SME expert industry mentor
| Batch | Start Date | Fee | Mode | Time |
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Why Choose CPMLF Course?
AI- ML Data Science Applications in Finance Podcast Series
AI- ML Data Science in Finance
Faculty for Machine Learning for Finance
SANJAY BHATIA
Background
- Director in UBS - Risk Modelling & Analytics, Model Risk Management & Control, Chief Risk Office (CRO) Function
- MBA-Finance & MSc in Machine Learning & Artificial Intelligence from Liverpool John Moores University (LJMU)
- Post-Graduate Diploma in Machine Learning & Artificial Intelligence from IIIT-Bangalore
- Domain SME on Credit Risk , Derivatives Counterparty Credit Risk, Derivative Pricing, Stochastic Modelling, Stress Testing
ML Expertise (Teaching ML for Quantitative Finance & Risk Management)
- Financial Prediction (Regression & Classification ) - Lasso/Ridge Regression, CART Decision Trees, Ensemble Learning (Bagging & Boosting) & Support Vector Machines (SVM)
- Financial Time Series Forecasting - (Recurrent) Neural Networks, RNN-LSTM, RNN-GRU, Hybrid-RNN-LSTM-GRU
- Financial Instrument Pricing - Non-Linear & High Dimensional Derivative Pricing using Neural Networks
- ML Model Optimization – Hyperparameters Tuning K-Fold Cross-Validation, Stochastic Gradient Descent, Convergence etc.
- Regulatory & Industry ML Adoption, Challenges & Use Cases – Model Explainibility, Performance Evaluation & Testing
Prof Rituparna Sen
Background
- - Associate Professor at the Applied Statistics Division, Indian Statistical Institute, Bangalore.
- - She worked as Assistant Professor at the University of California at Davis from 2004–2011
- - She has also taught courses in Chennai Mathematical Institute and Madras School of Economics.
- - An elected member of the International Statistical Institute
- - A council member of the International Society for Business and Industrial Statistics.
- - She has been awarded the Young Statistical Scientist Award by the International Indian Statistical Association, the Best - Student Paper Award by the American Statistical Association and the Women in Mathematical Sciences award by Technical University of Munich, Germany.
ML Expertise (Teaching ML Statistics for Computational Finance)
- - Authored A Book on Computational Finance with R
- - Authored 30+ Research Papers & Articles
- - Editor: Journal of Applied Stochastic Models in Business and Industry
- - Member of CAIML (Center for Artificial Intelligence and Machine Learning) at ISI
- - Associate Editor of several other journals
- - Guided several masters students on theses in the ML area
Dr. Arindam Chaudhuri
Background
- Worked as post doctoral fellow with Department of Computer Science, University of Copenhagen and Department of Computer Science, Technical University of Berlin.
- Worked as researcher with Siemens Research Labs Amsterdam and Samsung Research Labs at New Delhi & Bangalore.
- His current research interests include business analytics, artificial intelligence, machine learning, deep learning.
- He has published 4 research monographs and 60 articles in international journals and conference proceedings.
- He has served as reviewer for several international journals and conferences.
ML Expertise (Teaching ML Statistics for Computational Finance)
- Implemented Machine Learning Methods - Support Vector Machines, Artificial Neural Networks, Deep Learning Networks, Clustering, Genetic Algorithms and Evolutionary Computing with basic mathematical foundations of probability theory, fuzzy sets, rough sets, possibility theory and a variation of these for solution of various business problems.
- Integrated various artificial intelligence methods to form different soft computing frameworks such as neuro-fuzzy, fuzzy-genetic, neuro-genetic and rough-neuro-fuzzy-genetic.
- Successfully applied these methods for different categories of industrial problems such as decision theory, time series forecasting and prediction, image compression, sentiment analysis, recommendation systems, social networks analytics in order to achieve better results
Ritesh Chandra
Background
- - B. Tech. (IIT-Kanpur), PGDM (IIM-Calcutta), CFA
- - 15 years banking experience in risk management with domestic and MNC banks
- - Member, Board of Studies in the area of Finance at IMT-CDL, Ghaziabad
ML Expertise
- - Passionate about teaching. Has been conducting workshops / training programmes for the last 8 yrs in areas of Quantitative Finance, Financial Management, Risk Management and Machine Learning
- - Has contributed to a book on Applications of Blockchains in Financial Services industry
- - Has worked as visiting faculty with several institutions.
Rupal
Background
- B.Tech from IIT, Kanpur and Executive MBA from IIM Kozhikode.
- Currently working as Vice President, Fixed Income at one of the largest International Bank for their Corporate Investment Banking Division.
- Prior to this he was working as Assistant Vice President at Credit Suisse, Investment Banking Division.
- Regularly worked as an internal trainer in the organizations that he has worked in.
ML Expertise (Teaching ML Application for Financial Systems)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning & Data Science.
- His areas of interest are fixed income pricing, financial analytics and statistical learning
Dr. Hari
Background
- M.Sc. in Mathematics from B.H.U. Varanasi.
- Awarded four gold medals because of his outstanding performance in B.H.U.
- He has completed his Ph.D. in Mathematics from Indian Institute of Science, Bangalore.
- He has also worked with Deep Value (Algorithm trading firm).
- Currently involved with one of the world's leading analytics product company.
ML Expertise (Teaching ML Application for Algorithmic Trading)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning for Trading.
- ML applications for Algorithmic Trading, High Frequency Trading & Quantitative Analytics
Admission Process in Machine Learning for Finance Course
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Send Your Application
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Get on a call with a counsellor
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Wait for Application Acceptance
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Pay the fee & join the upcoming batch
Finance Your Study
Educational Loans
We are very happy to help you progress to greater heights in your career in every way possible. Education loans available at 0% interest for full time Indian residents. Easy EMI plans available.
Student Aid
Encourages the full time students to enter this domain, benefits, if you are still pursuing formal education.
Get Answers
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To whom this special program in Machine Learning (ML) is applicable ?
This exhaustive ML program broadly caters to anyone & everyone looking for career avenues in emerging field of Machine Learning & Data Science irrespective of industry/sector yet we’ve designed the application part to specifically cover implementation use cases in below sub-fields:
- Accounting & Finance
- Risk Management
- Portfolio Analytics
- Trading & Investment Analysis
- Regulatory & Internal Compliance
- Computational Finance & Financial Engineering -
What potential career avenues are available in the industry for the aspirants of this ML program?
This Machine Learning for Finance in Python Program caters to building practitioner-level skills to broadly cater to any & all ML & Data Science driven role families with a few real examples from the job market given below :
- Data Scientist for Financial/Accounting Decision Systems
- Machine Learning Model Designer for Financial Forecasting & Predictive Models
- Forensic Audit Data Scientist
- Fraud Analytics Machine Learning Expert
- Risk/Finance/Compliance Machine Learning Data Engineer
- Machine Learning Modeler for Quantitative Financial Models
- Machine Learning Driven Algorithmic Trader
- Machine Learning Model Validation Expert -
What skills one should possess to pursue Machine Learning & Data Science as a futuristic career path?
- Machine Learning Technical Know-How - E.g. Supervised/Unsupervised/Reinforcement/Deep-Learning Techniques & Methods
- Programming & Coding Skills - E.g. High Level & Powerful Languages Python, Julia, specific libraries/modules in SAS/R etc.
- Mathematical Skills - E.g. Linear Algebra, Matrix & Vector Operations, Multivariate Calculus, Optimization Problems
- Probability Theory & Statistical Skills - E.g. Probability Rules, Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian) -
What is the ML for Finance course structure & design?
ML for Finance Course Design FrameworkCovering the essentials to enable anyone/everyone to take this course- ML Programming Preliminaries
- ML Mathematical Preliminaries
- ML Probability & Statistical PreliminariesPersonalized live lectures from ML academicians & SME industry practitioners- ML Methodology
- ML Finance Use Case ImplementationFull blown pragmatic learning exposure- ML Python Labs with full time technical assistance
- ML Finance Use Case Implementation
- Capstone Project
- Hackathon Style Contests -
What kind of machine learning application use cases covered by CPMLF?
- Financial Time Series (Price, Volatility, VaR, ES etc.) Forecasting using Deep Learning Neural Networks (NN) algorithms
- Credit and counterparty credit risk modelling (PD, LGD, EAD, XVA, Margining etc.) using Ensemble Learning ML techniques
- High Dimensionality Reduction using Principal Component Analysis (PCA)/Autoencoder
- Derivative Pricing using Deep Learning Neural Networks (NN) algorithms
- Investment Portfolio Construction & Optimization using Reinforcement Learning
- Sentiment analysis on unstructured financial data using NLP algorithms
- Many others…. -
How machine learning (ML) models disrupt the world of quant finance?
- ML algorithms provides data-driven analytics versus assumption or theory based classical statistical models
- ML algorithms are more proficient to handle non linearities, multicollinearities, curvature risk and tail risk
- ML algorithms are more faster & efficient than statistical models
- ML algorithms allows for more dynamic estimations, parameterizations and calibrations
- ML algorithms can be made more consistent with front-office models leading to front-to-back integration
- ML algorithms facilitates real time portfolio risk management, asset pricing and valuation
- Many more….. -
What machine learning allied competencies are built by CPMLF course?
- Big Data & Processing Tech Stack
- Data Engineering, Exploration and Exploitation
- Data Synthetization & Simulations
- Data Insights, Visualization & Story Telling
- ML Devops - Design, Development, Debugging and Deployment
- ML model fine-tuning and optimization
- ML Research Expeditions
- Many more….. -
How CPMLF course is different from other such AIML course offerings?
- Upstarter primers & preliminaries for scratch-up skill building - ML Stats, Probability, Math & Programming
- Faculty includes - BFSI industry top senior ML practitioners & academia researchers
- ML pragmatic hands-on learning approach – focused problem solving, BFSI case studies, ML financial use case library etc.
- Exhaustive course coverage - ML Supervised, Semi-Supervised & Unsupervised Techniques
- 250+ hours of live classroom lectures
- Python prototyping labs & implementation workshops
- ML front-to-back tech stack & deployment
- Financial, Risk & Quants specific applications
- ML capstone projects, research topics & white paper writing
- Much more in-store…. -
What additional support is expected to be rendered by IIQF for CPMLF students?
- Capstone & research project mentoring
- BFSI industry ML core competencies awareness
- Placement opportunities for machine learning, data science, quantitative modelling & analytics role families
- Career guidance & coaching – skill evaluation, CV building, opportunity prospecting etc.
- Mock-up interview preparations & grooming
- Much more in-store….. -
What my CPMLF learning journey will look like if I don’t posses any of the basis skills & awareness on Machine Learning careers in finance?
- You will get lot of preparatory primers & preliminaries to get you the basic essential skills for machine learning
- You will get personal mentoring to get you doubts clarified & get you additional resources to build in basics right
- You will get the holistic know-how of programming & regular python labs practice sessions on coding problems
- You will get tailor made use cases & dataset aligned to your area of interest for prototyping
- You will get career guidance & counselling sessions to plan your aspirational career goal seeking
- You will get the right suitable job opportunities & targeted advise on how to shape up yourself as a prospective candidate
- Much more in-store….
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