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M-TECH in Computational Finance Machine Learning at Indian Institute of Technology Mandi
Mandi, Himachal Pradesh
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About the Specialization
What is Computational Finance & Machine Learning at Indian Institute of Technology Mandi Mandi?
This Computational Finance & Machine Learning (CFML) program at IIT Mandi focuses on equipping students with advanced skills at the intersection of quantitative finance, machine learning, and data science. It addresses the growing demand for professionals who can leverage sophisticated computational techniques to solve complex problems in the Indian financial sector, from algorithmic trading to risk management and blockchain applications. The program integrates theoretical foundations with practical, industry-relevant applications.
Who Should Apply?
This program is ideal for fresh engineering or science graduates with a strong quantitative background seeking entry into fintech, quant research, or data science roles in finance. It also caters to working professionals in finance, IT, or analytics looking to upskill with modern computational and machine learning methodologies. Additionally, it suits career changers aspiring to transition into the rapidly evolving field of financial technology, providing the necessary prerequisite knowledge and practical exposure.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including quantitative analysts, data scientists, algorithmic traders, risk managers, and blockchain developers in banks, hedge funds, fintech startups, and consulting firms. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals commanding significantly higher packages. The program fosters growth trajectories towards leadership roles in financial innovation and quantitative research, aligning with certifications like FRM or CFA at a foundational level.
Student Success Practices
Foundation Stage
Strengthen Quantitative & Programming Fundamentals- (Semester 1-2)
Dedicate time to mastering advanced linear algebra, probability, stochastic processes, and Python programming. Utilize online platforms and textbooks beyond lectures to solidify foundational knowledge, which is critical for complex financial and ML models.
Tools & Resources
NPTEL courses on Linear Algebra/Probability, LeetCode/HackerRank for coding practice, Python libraries like NumPy, Pandas
Career Connection
A robust foundation directly impacts your ability to understand and implement advanced algorithms, making you a strong candidate for quantitative analyst or data scientist roles.
Participate in Academic & Coding Competitions- (Semester 1-2)
Engage in data science or programming competitions (e.g., Kaggle, ICPC, D2C) to apply learned concepts in practical scenarios. Collaborate with peers to develop problem-solving skills and enhance your resume with demonstrable project experience.
Tools & Resources
Kaggle for datasets and competitions, GitHub for version control and collaboration, Local university hackathons
Career Connection
Showcases practical application skills to recruiters, improves competitive programming abilities, and builds a portfolio of problem-solving experience, crucial for fintech and quant roles.
Proactive Networking and Industry Insights- (Semester 1-2)
Attend webinars, workshops, and guest lectures by industry experts in finance and machine learning. Start building a professional network on LinkedIn and connect with alumni to gain insights into industry trends and potential career paths in India.
Tools & Resources
LinkedIn, Industry-specific webinars (e.g., NASSCOM, FICCI), Alumni network events
Career Connection
Early networking can lead to internship opportunities, mentorship, and a better understanding of the Indian job market landscape, crucial for informed career decisions.
Intermediate Stage
Deep Dive into Financial Domain Knowledge- (Semester 2-3)
Beyond coursework, read financial news, analyze market trends, and understand regulatory frameworks specific to India. Combine this with hands-on practice in financial modeling using tools and datasets relevant to the Indian market.
Tools & Resources
Bloomberg Terminal (if available at IIT Mandi), NSE/BSE websites for data, Financial Times/Economic Times
Career Connection
Bridging the gap between theoretical ML and financial realities makes you a valuable asset, particularly for roles in investment banking, risk management, and regulatory compliance in India.
Engage in Relevant Research Projects & Internships- (Semester 2-3)
Seek out research projects with faculty or summer internships in fintech companies, banks, or quantitative hedge funds in India. Focus on applying CFML concepts to real-world financial datasets and challenges.
Tools & Resources
IIT Mandi''''s career development cell, Company career portals, Research publications platforms
Career Connection
Internships provide crucial industry exposure, build a professional network, and often lead to pre-placement offers, significantly boosting employability in the Indian finance sector.
Develop Specialized Machine Learning Skills- (Semester 2-3)
Choose electives strategically that align with your career interests (e.g., Deep Learning, Reinforcement Learning, NLP for finance). Work on mini-projects demonstrating proficiency in these specialized areas, perhaps focusing on Indian market data.
Tools & Resources
TensorFlow/PyTorch, Hugging Face for NLP models, Online specialized courses (Coursera, edX)
Career Connection
Specialized skills differentiate you in a competitive market, enabling you to target niche roles like AI quant, deep learning engineer for trading, or NLP analyst in financial services.
Advanced Stage
Intensive M.Tech Project Work & Publication Focus- (Semester 3-4)
Concentrate intensely on your M.Tech project, aiming for a high-quality outcome. Consider presenting your research at national/international conferences or publishing in relevant journals, showcasing original contribution.
Tools & Resources
LaTeX for thesis writing, Mendeley/Zotero for referencing, Conference proceedings (e.g., IEEE, ACM)
Career Connection
A strong project can be a major talking point in interviews, demonstrating advanced research and problem-solving capabilities, and can lead to R&D roles or higher studies.
Comprehensive Placement Preparation- (Semester 3-4)
Begin mock interviews, resume reviews, and aptitude test preparation early. Focus on behavioral, technical (ML, quant finance), and case study questions relevant to Indian financial firms. Leverage campus placement cells and alumni support.
Tools & Resources
IIT Mandi placement cell resources, Glassdoor for interview experiences, Quantitative finance interview prep books
Career Connection
Thorough preparation directly translates to successful placements in desired companies, securing competitive salaries and kickstarting your career in India''''s dynamic financial tech space.
Build a Robust Professional Portfolio- (Semester 3-4)
Compile all projects, competition achievements, and research work into a well-documented online portfolio (e.g., GitHub, personal website). Emphasize your contributions and the impact of your work, especially projects with real-world financial implications.
Tools & Resources
GitHub profile for code, Personal website/blog, Medium for project write-ups
Career Connection
A strong portfolio acts as a live resume, visually demonstrating your skills and experience to potential employers, making you a standout candidate for advanced roles in fintech.
Program Structure and Curriculum
Eligibility:
- B.Tech/B.E. or equivalent degree in any engineering discipline, or M.Sc. in Physics/Mathematics/Statistics/Computer Science/Information Technology or equivalent. Candidates with a B.A./B.Sc. degree in Mathematics/Statistics/Computer Science/Information Technology must have a minimum of two years of work experience in the relevant domain. Candidates must also have a valid GATE score or be sponsored by an industry.
Duration: 4 semesters / 2 years
Credits: 71 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA511 | Probability and Stochastic Processes | Core | 4 | Probability spaces and axioms, Random variables and distributions, Joint and conditional distributions, Stochastic convergence, Stochastic processes, Markov chains and Martingales |
| MA501 | Linear Algebra and Optimization | Core | 4 | Vector spaces and linear transformations, Eigenvalues, eigenvectors, SVD, Convex sets and functions, Linear Programming and Simplex method, Duality and KKT conditions, Non-linear optimization techniques |
| CS548 | Machine Learning | Core | 4 | Supervised and Unsupervised Learning, Regression and Classification algorithms, Support Vector Machines (SVMs), Decision Trees and Ensemble methods, Neural Networks fundamentals, Clustering and Dimensionality Reduction |
| CS561 | Programming for Data Science | Core | 4 | Python programming for data science, Data structures and algorithms, Data manipulation with NumPy and Pandas, Data visualization with Matplotlib/Seaborn, Web scraping and API interaction, Introduction to Big Data tools |
| HS6xx | Humanities and Social Sciences Elective | Elective | 3 | Economics and financial systems, Ethics in technology and finance, Organizational behavior, Social impact of technology, Communication skills, Public policy |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CF501 | Financial Markets and Instruments | Core | 4 | Structure of financial markets, Money and capital markets, Fixed income securities and equities, Derivatives: Futures, Options, Swaps, Foreign exchange markets, Portfolio management concepts |
| CF502 | Computational Finance | Core | 4 | Stochastic calculus and Ito''''s Lemma, Black-Scholes-Merton model, Numerical methods for option pricing, Monte Carlo simulation, Binomial and Trinomial trees, Volatility modeling and calibration |
| CF503 | Statistical Methods for Finance | Core | 4 | Time series analysis, ARMA and ARIMA models, ARCH and GARCH models, Cointegration and VAR models, Regression analysis in finance, Hypothesis testing and Bayesian methods |
| CF504 | Introduction to Blockchain Technology | Core | 4 | Fundamentals of cryptography and hashing, Distributed ledger technology principles, Bitcoin and Ethereum architecture, Smart contracts and DApps, Consensus mechanisms (PoW, PoS), Blockchain security and privacy |
| CF601 | Financial Econometrics | Elective | 4 | Advanced regression techniques, Panel data analysis in finance, High-frequency data analysis, Volatility forecasting models, Market microstructure econometrics, Event studies in financial markets |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CF699 | M.Tech Project (Part-I) | Project | 8 | Problem identification and literature review, Research methodology design, Data collection and preprocessing, Initial model development, Project proposal and presentation, Ethical considerations in research |
| CF602 | Credit Risk Modeling | Elective | 4 | Concepts of credit risk, Default probability estimation, Merton''''s structural model, Reduced-form models, Credit derivatives pricing, Basel Accord regulations |
| CS541 | Deep Learning | Elective | 4 | Neural network architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers and attention mechanisms, Generative Adversarial Networks (GANs), Optimization techniques for deep learning |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CF799 | M.Tech Project (Part-II) | Project | 12 | Advanced model implementation and experimentation, Results analysis and interpretation, Performance evaluation and benchmarking, Thesis writing and documentation, Final project defense, Publication and dissemination strategies |
| CF604 | Algorithmic Trading | Elective | 4 | Quantitative trading strategies, Order execution algorithms, Market impact and liquidity, Backtesting and simulation, High-frequency trading techniques, Risk management in algorithmic trading |
| CF603 | Market Microstructure | Elective (Additional Option) | 4 | Order book mechanics, Price discovery process, Bid-ask spread analysis, Liquidity measures and models, High-frequency data analysis, Regulatory impact on market structure |
| CF605 | Reinforcement Learning in Finance | Elective (Additional Option) | 4 | Markov Decision Processes (MDPs), Q-learning and SARSA, Deep Reinforcement Learning (DRL), Policy gradient methods, Portfolio optimization with RL, Optimal execution strategies |
| CF606 | Data Analytics for Finance | Elective (Additional Option) | 4 | Big data technologies in finance, Financial data sources and APIs, Data warehousing and ETL processes, Data mining techniques, Predictive analytics for financial forecasting, Visualization of financial data |
| CF607 | Advanced Blockchain and Cryptocurrency | Elective (Additional Option) | 4 | Advanced cryptographic primitives, Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), Cross-chain interoperability, Blockchain scalability solutions, Regulatory landscape of cryptocurrencies |
| MA603 | Bayesian Machine Learning | Elective (Additional Option) | 4 | Bayesian inference fundamentals, Prior and posterior distributions, Markov Chain Monte Carlo (MCMC), Variational Inference, Gaussian Processes, Bayesian neural networks |
| MA604 | Time Series Analysis | Elective (Additional Option) | 4 | Advanced ARIMA models, State-space models, Spectral analysis of time series, Non-linear time series models, Forecasting methods, Multivariate time series |
| CS549 | Natural Language Processing | Elective (Additional Option) | 4 | Text preprocessing and tokenization, Language models and embeddings (Word2Vec, BERT), Sentiment analysis and opinion mining, Text classification and clustering, Information extraction, Machine translation basics |
| CS550 | Computer Vision | Elective (Additional Option) | 4 | Image processing fundamentals, Feature extraction and descriptors, Object detection and recognition, Image segmentation, Deep learning for computer vision, Generative models for images |
| CS601 | Advanced Machine Learning | Elective (Additional Option) | 4 | Ensemble learning (boosting, bagging), Kernel methods and SVM extensions, Graphical models (HMM, Bayes Nets), Causality and inference, Meta-learning, Reinforcement learning advanced topics |
| CS602 | Big Data Analytics | Elective (Additional Option) | 4 | Hadoop ecosystem (HDFS, MapReduce), Apache Spark for big data processing, NoSQL databases (Cassandra, MongoDB), Stream processing (Kafka, Flink), Data warehousing strategies, Distributed machine learning platforms |
| EE603 | Digital Signal Processing | Elective (Additional Option) | 4 | Discrete-time signals and systems, Z-transform and DFT/FFT, Digital filter design (FIR, IIR), Adaptive filters applications, Power spectrum estimation, Multirate signal processing |
