Course Number & Title: DA461 & Bioinformatics L-T-P-C: 2-0-2-6 Pre-Requisite: Course Content/ Syllabus Primer on molecular biology: Structure and function of nucleic acids and protein; genes and gene expression; the central dogma of biology; Sequencing, structure determination, and bioinformatics databases. Sequence alignment: Pairwise and multiple sequence alignment; Dynamic Programming; Global alignment; Local alignment; Scoring matrices and gap penalty; Carrillo–Lipman algorithm; Feng–Doolittle algorithm; CLUSTAL; Sequence logo. Pattern detection: Gibbs sampler; Hidden Markov model; Profile HMM Alignment. Phylogenetic analysis: Molecular evolution, homolog, orthologs, paralogs; Rooted and unrooted phylogenetic tree; Maximum parsimony method; Distance-based method; Genome assembly and next-generation sequencing: Shortest superstring approach; Overlap graph approach; de Bruijn graph approach; NGS read mapping; RNA-seq read mapping; Peak calling method. The lectures will focus on the well-established algorithms in these topics, and the laboratory exercises will supplement those lectures with programming assignments and mini projects. Texts: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. Neil C. Jones, and Pavel A. Pevzner, An Introduction to Bioinformatics Algorithms, 1st Edition, ANE Books, 2009. 2. Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison, Biological Sequence Analysis, Cambridge University Press, 1st Edition, 1998. 3. Wing-Kin Sung, Algorithms For Next-Generation Sequencing, 1st Edition, CRC Press, 2020. References: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. Phillip Compeau and Pavel Pevzner, Bioinformatics Algorithms: An Active Learning Approach, Vol. I, 2nd Edition, Active Learning Publisher, 2015 2. Mourad Elloumi and Albert Y. Zomaya, Algorithms In Computational Molecular Biology, 1st Edition, Wiley, 2011.
DA462 & Data Analytics For Finance
L-T-P-C: 3-0-0-6 Pre-Requisite: Course Content/ Syllabus
Asset pricing models, binomial model, geometric Brownian motion; Financial derivatives, options, forwards & futures, swaps; Black-Scholes equation, valuation of forwards, futures and swaps; Monte Carlo simulation, supervised learning for asset price prediction, ML model for pricing derivatives; Financial risk management, Basel regulations, credit risk, market risk, operational risk; ML in financial risk management, Value-at-Risk (VaR), estimating credit and operational risk. Texts: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. John C. Hull and Sankarshan Basu, Options, Futures & Other Derivatives, 10th Edition, Pearson, 2018. 2. Hariom Tatsat, Sahil Puri and Brad Lookabaugh, Machine Learning and Data Science Blueprints for Finance, 1st Edition, O'Reilly Media, 2020. 3. Abdullah Karasan, Machine Learning for Financial Risk Management with Python, 1st Edition, O'Reilly Media, 2021. References: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. Marek Capinski and Tomasz Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, 2nd Edition, Springer, 2010. 2. John C. Hull, Risk Management and Financial Institutions, 4th Edition, Wiley, 2015. 3. Paul Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2010.
Markowitz portfolio theory, Capital Asset Pricing Model, asset ranking, performance analysis; Portfolio management through clustering, RL based algorithm for portfolio allocation;
L-T-P-C: 3-0-0-6 Pre-Requisite: Data: Protection rational & genesis, data protection in India (judicial developments on right to privacy, legislative developments); Territorial and Personal Scope; Personal data; Sensitive personal data; Processing of data; Processing of sensitive data; Rights: Introduction, right to object to processing, right to be forget; Case studies. Fairness: Introduction, sources of unfairness, definitions; Metrics for fairness, fair data; pre-processing methods; In-processing methods; post-processing methods; Model auditing for fairness; ML models and privacy; ML models and security; Fair product design & development; Laws for ML; Compliance tools: Anonymisation, Privacy by design. Accountability & Ethics: Introduction, Guidelines in AI ethics; AI in practice; Advances in AI ethics; Transparency (Explainability): Importance of explainability in AI systems, Case studies; Accuracy-interpretability tradeoff in machine learning; Different types of interpretability approaches: Rule-based, Prototype-based, Feature importance-based, post-hoc explanations. Texts: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. Aileen Nielsen, Practical Fairness, O'Reilly Media, Inc, 2020. 2. Solon Barocas, Moritz Hardt, Arvind Narayanan, Fairness and Machine Learning – Limitations and Opportunities, https://fairmlbook.org/, 2019. References: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.) 1. Cathy O’Nell, Weapons of Math Destruction: How Big Data Increases Inequalities and Threatens Democracy, Penguin Publisher, July 2017. 2. White paper of the committee of experts on data protection framework for India. December, 2017, https://www.meity.gov.in/writereaddata/files/white_paper_on_data_protect... 3.