Seventh Semester B.Tech Core Course Syllabus

Course Code: DA461 Course Name: Bioinformatics Credits: 2-0-2-6
Pre-requisite: None
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.
Textbooks:
  • Neil C. Jones, and Pavel A. Pevzner, An Introduction to Bioinformatics Algorithms, 1st Edition, ANE Books, 2009.
  • Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison, Biological Sequence Analysis, Cambridge University Press, 1st Edition, 1998.
  • Wing-Kin Sung, Algorithms For Next-Generation Sequencing, 1st Edition, CRC Press, 2020.
References:
  • Phillip Compeau and Pavel Pevzner, Bioinformatics Algorithms: An Active Learning Approach, Vol. I, 2nd Edition, Active Learning Publisher, 2015.
  • Mourad Elloumi and Albert Y. Zomaya, Algorithms In Computational Molecular Biology, 1st Edition, Wiley, 2011.

Course Code: DA462 Course Name: Data Analytics for Finance Credits: 3-0-0-6
Pre-requisite:
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; Markowitz portfolio theory, Capital Asset Pricing Model, asset ranking, performance analysis; Portfolio management through clustering, RL based algorithm for portfolio allocation; 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.
Textbooks:
  • John C. Hull and Sankarshan Basu, Options, Futures & Other Derivatives, 10th Edition, Pearson, 2018.
  • Hariom Tatsat, Sahil Puri and Brad Lookabaugh, Machine Learning and Data Science Blueprints for Finance, 1st Edition, O'Reilly Media, 2020.
  • Abdullah Karasan, Machine Learning for Financial Risk Management with Python, 1st Edition, O'Reilly Media, 2021.
References:
  • Marek Capinski and Tomasz Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, 2nd Edition, Springer, 2010.
  • John C. Hull, Risk Management and Financial Institutions, 4th Edition, Wiley, 2015.
  • Paul Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2010.

Course Code: DA421 Course Name: FATE in AI Models Credits: 3-0-0-6
Pre-requisite: None
Syllabus: 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.
Textbooks:
References: