MMSc-BMI Approved Electives

BETH 706 - Health, Law, Policy and Bioethics (HMS: 4 credits)

Fall Semester Course

This course is a survey-style introduction to legal topics in health policy and bioethics. It requires no experience in law, and topics covered could include legal aspects of the doctor-patient relationship, medical malpractice, privacy issues, health care finance, end-of-life issues, organ donation, disability, mental health, medical product regulation, and intellectual property. The course will not cover issues in reproductive ethics or human subjects research regulation as those are covered in other Master of Bioethics core classes. Students will be evaluated via class participation and written work. Sessions will be a mix of lecture and seminar-style, with occasional guest speakers.

BMI 706 - Data Visualization for Biomedical Applications (HMS: 2 credits)

Fall Semester Course

Data visualization is an essential component of the analysis toolkit in any data-driven research endeavor. As the primary interface through which analysts are consuming data, data visualization can facilitate new discoveries but also mislead, bias, and slow down progress if done poorly. This visualization course will focus on the role of data visualization in biomedical data analysis applications and also cover the principles of perception and cognition relevant for data visualization. It will also introduce the data visualization design process, and visualization tools and techniques used in biomedical informatics. Major topics include interaction techniques and implementations, high-dimensional data, networks, genomes, time and event sequences, and common generic visualization systems. Students are expected to complete class readings, a final project, and make a final presentation.

^{Computational Course}

APCOMP 209A - Introduction to Data Science (FAS: 4 credits, HMS: 4 credits)

Fall Semester Course

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. 

^{Computational Course}

APCOMP 215 - Advanced Practical Data Science (FAS: 4 credits, HMS: 4 credits)

Fall Semester Course

The primary objective of this course is to provide a comprehensive understanding of the Deep Learning process in a practical, real-world context. With a strong emphasis on Machine Learning Operations (MLOps), this course not only reviews existing Deep Learning flows, but also enables students to build, deploy, and manage applications that leverage these models effectively. In the rapidly evolving field of data science, merely creating powerful predictive models is not enough. Efficiently deploying and managing these models in production environments - a practice often referred to as MLOps - has become an essential skill. MLOps bridges the gap between the development of Machine Learning (ML) models and their operation in production settings, combining practices from data science, data engineering and software engineering. This course is built upon the model of balancing conceptual understanding, theoretical knowledge, and hands-on implementation. It introduces students to the iterative process of model development, testing, deployment, monitoring, and updating, ensuring they acquire a strong foundation in MLOps principles.

^{Computational Course}

COMPSCI 1870 - Introduction to Computational Linguistics and Natural-Language Processing  (FAS: 4 credits, HMS: 4 credits)

Fall Semester Course

Natural-language-processing applications are ubiquitous – from digital assistants like Siri or Alexa, to machine translation systems like Google Translate, to fluent conversational systems like ChatGPT, Claude, and Gemini. How do such systems work?

This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as these. You will work with ideas from linguistics, statistical modeling, machine learning, and neural networks, especially the technologies behind current large language models (LLMs). The course is lab- and project-based, with students working primarily in small teams, and culminates in the building and testing of a full transformer-based question-answering system.

^{Computational Course}

COMPSCI 2881R - Topics in Foundations of ML: Mathematical & Engineering Principles for Training Foundation Models (FAS: 4 credits, HMS: 4 credits)

Fall Semester Course

This will be a graduate level course on challenges in alignment and safety of artificial intelligence. We will consider both technical aspects as well as questions on societal and other impact on the field. This is a fast-moving area and it will be a fast-moving course. I will expect students to be be able to pick up technical knowledge on their own. In a sense, the programming language for this course will be English: students will be allowed and encouraged to use AI tools for all homework and assignments. On the other hand, this means that expectations will be raised: it may well be the case that I would expect you to do in a week assignments that in previous years would have taken a month.

^{Computational Course}

STAT 110 - Introduction to Probability  (FAS: 4 credits, HMS: 4 credits)

Fall Semester Course

A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes' Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.

^{Computational Course}

BST 201 - Introduction to Statistical Methods  (HSPH: 5 credits, HMS: 4 credits)

Fall Semester Course

Covers basic statistical techniques that are important for analyzing data arising from epidemiology, environmental health and biomedical and other public health-related research. Major topics include descriptive statistics, elements of probability, introduction to estimation and hypothesis testing, nonparametric methods, techniques for categorical data, regression analysis, analysis of variance, and elements of study design. Applications are stressed. Designed for students desiring more emphasis on theoretical developments. Background in algebra and calculus strongly recommended.

^{Computational Course}

BST 210 - Applied Regression Analysis  (HSPH: 5 credits, HMS: 4 credits)

Fall Semester Course

Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.

^{Computational Course}

BST 222 - Basics of Statistical Inference (HSPH: 5 credits, HMS: 4 credits)

Fall Semester Course

This course will provide a basic, yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics to be covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material will be motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required.

^{Computational Course}

BST 249 - Bayesian Methodology in Biostats (HSPH: 5 credits, HMS: 4 credits)

Fall Semester Course

General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis.

^{Computational Course}

EPI 519 - Evolutionary Epidemiology of Infectious Disease (HSPH: 2.5 credits, HMS: 2 credits)

Fall Semester Course

Like all living things, pathogens have evolved by natural selection. The application of evolutionary principles to infectious disease epidemiology is crucial to such diverse subjects as outbreak analysis, the understanding of how different genomic combinations of virulence and drug resistance determinants emerge, and how selection acts to produce successful pathogens that balance the costs and benefits of virulence and transmission. The goal of this course is to introduce basic evolutionary concepts, highlighting the importance of transmission to the fitness as illustrated by comparisons of the adaptive process among different sorts of pathogenic microorganisms. Students will also learn the basics of phylogenetic sequence analysis for the study of outbreaks and transmission, and the construction of simple mathematical models that probe the adaptive process.

GHP 220 - Intro to Demographic Methods (HSPH: 2.5 credits, HMS: 2 credits)

Fall Semester Course

This is an introductory level class on the analysis of mortality, fertility and population change. It is required for all masters' and doctoral students in the department of Global Health and Population. Students are introduced to the core literature in this field through lectures, and assigned readings selected from peer-reviewed journals and textbooks. Together, these provide a graduate-level introduction to the principle sources and characteristics of population data and to the essential methods used for the analysis of population problems. The emphasis throughout is on understanding the key processes, models and assumptions used primarily for the analysis of demographic components. Practical training will be given through a required weekly laboratory session, assignments, and a final examination. Examples presented in class and used in assignments are drawn from several countries, combining both developed and developing in assignments are drawn from several countries, combining both developed and developing world realities.

HPM 206 - Economic Analysis (HSPH: 5 credits, HMS: 4 credits)

Fall Semester Course

Economic concepts are everywhere in our daily lives and in public health and health care policy. This course is designed to introduce economic concepts, focusing primarily on microeconomics, or the behavior of individuals and organizations interacting with each other. We will focus on the uses and limitations of the economic approach with applications in public health and medical care. 

HPM 282 - Innovative Problem Solving & Design Thinking in Healthcare (HSPH: 2.5 credits, HMS: 2 credits)

Fall Semester Course

Innovative problem solving is a critical skill for healthcare leaders confronting disruptive change and operating in increasingly complex and fast-paced environments. The capacity to innovate is essential to effectively serving patients, improving outcomes and developing sustainable organizations. Design thinking is a disciplined approach to innovation that focuses intensely on the intersection of human needs and values, technical feasibility and strategic viability to create end-user value and pursue market opportunity. Healthcare organizations such as the Mayo Clinic, Kaiser Permanente and the UK’s National Health Service (NHS) as well as Ministries of Health in many countries have turned to design thinking to improve patient care and citizen health.

In contrast to a traditional approach to problem solving that focuses on deciding among known solutions, an innovative approach seeks the best solution possible given available resources, time, and team competencies. Innovative problem solving maximizes learning to reduce uncertainty by focusing on generation of new alternatives, experimentation, and exploration of multiple solutions.

Learning will occur through a mix of individual and group exercises in class as well as a series of graded and ungraded assignments that enable you to effectively use innovation tools, acquire skills, and adopt mindsets that complement the analytical approaches you have developed in other courses.

RDS 280 - Decision Analysis for Health/Medical Practice (HSPH: 2.5 credits, HMS: 2 credits)

Fall Semester Course

This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in high-income and low-income settings.

SBS 288 - Qualitative Research Methods in Public Health (HSPH: 2.5 credits, HMS: 2 credits)

Fall Semester Course

What students can expect from this course: Qualitative research can be used alone or in combination with quantitative research to investigate public health questions. This introductory course will provide students with an overview of the range of important conceptual and practical issues associated with qualitative research, including providing general familiarity with the design of qualitative studies and conduct of commonly-used qualitative methods. The course begins by examining the variety of potential uses of qualitative methods in public health research and diverse qualitative research approaches. The course then explores specific topics, including: developing research questions; ethics in qualitative research; “entering” the community to conduct qualitative research; role of theory; ensuring study rigor; selecting and implementing qualitative data collection methods (participant observation, different types of semi-structured interviews and focus groups); writing open-ended questions; sampling; data management and analysis; publishing results; writing research proposals; and considerations for choosing qualitative methods for mixed-methods qualitative or mixed-methods qualitative/quantitative studies. 
Students should come to class prepared to apply concepts from readings and lectures through participation in student- and instructor-led class discussions and small group activities that will occur during every class period. In addition, students will demonstrate application of concepts through completion of written assignments that involve practice of qualitative methods.
What this course is not: As this is an introductory course on qualitative research that provides an overview of all pertinent topics to foster familiarity with this research approach as a whole, the course cannot dwell deeply on any one topic. Students who are looking for in-depth training on a particular step in qualitative research, such as how to analyze their own qualitative dataset, or how to use qualitative coding software, are advised to select a different course.  

1.010B - Causal Inference for Data Analysis  (MIT: 6 units, HMS: 2 credits)

Fall Semester Course

Introduces causal inference with an emphasis on probabilistic systems analysis. Readings about conceptual and mathematical background are given in advanced of each class. Focused on understanding theory based on real-world applications. The subject is project-based and focused on cause-effect relationships, understanding why probabilistic outcomes happen. Topics include correlation analysis, Reichenbach's principle, Simpson's paradox, structural causal models and graphs, interventions, do-calculus, average causal effects, dealing with missing information, mediation, and hypothesis testing. Students taking graduate version complete additional assignments.

^{Computational Course}

6.796 - Deep Learning  (MIT: 12 units, HMS: 4 credits)

Fall Semester Course

Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics.

^{Computational Course}

9.66 - Computational Cognitive Science (MIT: 12 units, HMS: 4 credits)

Fall Semester Course

Introduction to computational theories of human cognition. Focuses on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks include Bayesian and hierarchical Bayesian models, probabilistic graphical models, nonparametric statistical models and the Bayesian Occam's razor, sampling algorithms for approximate learning and inference, and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project.

^{Computational Course}

15.128 - Revolutionary Ventures: How to Invent and Deply Transformative Technologies (MIT: 9 units, HMS: 4 credits)

Fall Semester Course

Seminar on envisioning and building ideas and organizations to accelerate engineering revolutions. Focuses on emerging technology domains, such as neurotechnology, imaging, cryotechnology, gerontechnology, and bio-and-nano fabrication. Draws on historical examples as well as live case studies of existing or emerging organizations, including labs, institutes, startups, and companies. Goals range from accelerating basic science to developing transformative products or therapeutics. Each class is devoted to a specific area, often with invited speakers, exploring issues from the deeply technical through the strategic. Individually or in small groups, students prototype new ventures aimed at inventing and deploying revolutionary technologies.

CSB .100 - Topics in Computational and Systems Biology  (MIT: 12 units, HMS: 4 credits)

Fall Semester Course

Seminar based on research literature. Papers covered are selected to illustrate important problems and varied approaches in the field of computational and systems biology, and to provide students a framework from which to evaluate new developments. Preference to first-year CSB PhD students.

HST 504 - Topics in Computational Molecular Biology   (MIT: 12 units, HMS: 4 credits)

Fall Semester Course

Covers current research topics in computational molecular biology. Recent research papers presented from leading conferences such as the International Conference on Computational Molecular Biology (RECOMB) and the Conference on Intelligent Systems for Molecular Biology (ISMB). Topics include original research (both theoretical and experimental) in comparative genomics, sequence and structure analysis, molecular evolution, proteomics, gene expression, transcriptional regulation, biological networks, drug discovery, and privacy. Recent research by course participants also covered. Participants will be expected to present individual projects to the class.

HST 507 - Advanced Computational Biology: Genomes, Networks, Evolution (MIT: 12 units, HMS: 4 credits)

Fall Semester Course

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks.

Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication.

^{Computational Course}

BMI 707 - Deep Learning for Biomedical Data (HMS: 2 credits)

Spring Semester Course

Deep learning is a branch of machine learning that leverages multiple layers of data representations to capture complex data patterns at various levels of abstraction. Inspired by the organization of neurons in biological systems, deep learning has demonstrated exceptional performance in many tasks, including image classification, natural language processing, and protein structure prediction. This course will introduce the fundamental principles of deep neural networks and GPU computing, discuss convolutional neural networks and transformer architectures, and examine key biomedical applications. Students are expected to be familiar with linear algebra and machine learning and will participate in a group project.

BMI 709 - Creating Biomedical Dashboards Using R Shiny (HMS: 2 credits)

Fall and Spring Semester Course

Biomedical research projects and pipelines often generate output that requires additional interpretation or benefit from interactive and customizable summarization. Web-based dashboards are a convenient way of providing an easy interface to interact with this data, eliminating the need for direct interaction with the underlying data processing.

This course will teach you Shiny, a powerful dashboard framework on top of R that lets you build custom, web-based applications that can serve as a front-end to any R code. You will learn how to build apps from scratch or convert existing R scripts into a Shiny application.

Students will need to enter the class with foundational R programming skills to start writing Shiny applications. Familiarity with Git and GitHub is highly recommended as this is how course materials and assignments in this class will be distributed and evaluated.

BMI 710 - Single-Cell Analysis for Functional Genomics of Disease (HMS: 2 credits)

Spring Semester Course 

Single-cell technologies promise unparalleled insights into human biology, but first, these data require a new computational and statistical toolkit.  In this course, we will compare single-cell and bulk data analysis paradigms, and explore new methods to quality control, analyze, and interpret single-cell-RNA-sequencing data.  Students will learn about single-cell technologies and experimental design, build pipelines to process sequencing data, and use R packages for quality control and analysis.  Students will learn about frameworks for interpreting single-cell data - eg., trajectories, differential abundance - and use them to answer biological questions with single-cell datasets in diverse domains.  We will conclude by exploring the application of single-cell technologies to disease cohorts with spatial profiling, reference mapping, and multimodal approaches.  Class activities will include lectures, coding activities, and paper discussions.

BMI 741 - Health Information Technology Innovation: From Ideation to Implementation (HMS: 4 credits)

Spring Semester Course

As the US healthcare system moves from a fee-for-service to value-based reimbursement system and seeks to deliver higher quality care more efficiently, there is increased need and opportunity for innovation. Clinical Informatics analyzes, designs, implements, and evaluates information and communication systems to enhance health outcomes, to improve patient care, and to enable healthcare transformation. This class applies health information technology (HIT) to solve health care problems, teaching the skills to identify health care needs and pain points, design technology-based solutions (new solution, optimize existing system, or purchase vendor solution), and lead successful implementations. Course activities will include lectures, panel discussions, and laboratory sessions. Expert speakers from hospitals, technology vendors, and start-ups will present real-world examples and share lessons learned. Course participants will complete a longitudinal group project proposing an HIT innovation project; this project may serve as the basis for start-ups, fellowship projects, and research theses.

NCE 512 - Big Data and Machine Learning in Healthcare Applications (HMS: 2 credits)

Spring Semester Course

This course is designed as a critical, hands-on exploration of major healthcare data sources and current state-of-the-art algorithms. The students will learn how to use data for solving the most challenging healthcare problems,  to achieve real implementation gains, and to avoid common pitfalls. We will study a wide range of real problems (in clinical workflows, patient records, medical images), to see how their data patterns can be found and used in the most meaningful way. The course will be based on real data, including healthcare operations records, patient demographics, clinical records (vital signs, lab results), and medical imaging. Note that all data will come from public and anonymized sources, so no special medical records access/clearance will be required.

APCOMP 209B - Advanced Topics in Data Science (FAS: 4 credits, HMS: 4 credits)

Spring Semester Course

Note: MBI students have successfully enrolled in this course without taking part 1 in the fall. Please make sure to mention your degree program and programming preparation undergone in BMI 714 when submitting petitions for this course. 

Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, and unsupervised learning. 

COMPSCI 1810 - Machine Learning (FAS: 4 credits, HMS: 4 credits)

Spring Semester Course

Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.

GENETICS 228 - Genetics in Medicine: From Bench to Bedside (FAS: 4 credits, HMS: 4 credits)

Spring Semester Course

Focus on translational medicine: the application of basic genetic discoveries to human disease. Each three-hour class will focus on a specific genetic disorder and the approaches currently used to speed the transfer of knowledge from the laboratory to the clinic. Each class will include a clinical discussion, a patient presentation if appropriate, followed by lectures, a detailed discussion of recent laboratory findings and a student-led journal club. Lecturers will highlight current molecular, technological, bioinformatics and statistical approaches that are being used to advance the study of human disease. There is no exam. Students will present one paper per session in a journal club style.

Attendance and active participation for the duration of all class meetings is required. If you are unable to attend class or cannot be present for the entire session, you are expected to contact the course instructor. Two incomplete or missed sessions will result in a failing grade. Please do not sign up if you know you will have to miss 2 or more sessions. For more information visit https://ecor.mgh.harvard.edu/Default.aspx?node_id=375 

MICROBI 210 - Microbial Sciences: Chemistry, Ecology and Evolution (FAS: 4 credits, HMS: 4 credits)

Spring Semester Course

This is an interdisciplinary graduate-level and advanced undergraduate-level course in which students explore topics in molecular microbiology, microbial diversity, host-microbe associations in health and disease, and microbially-mediated geochemistry in depth. This course will be taught by faculty from the Microbial Sciences Initiative. Topics include the origins of life, biogeochemical cycles, microbial diversity, and ecology. Course will limit enrollment to 20 students.

BST 210 - Applied Regression Analysis (HSPH: 5 credits, HMS: 4 credits)

Spring Semester Course

Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.

BST 223 - Applied Survival Analysis (HSPH: 5 credits, HMS: 4 credits)

Spring Semester Course

BST 223 is a course on survival analysis, or more generally time-to-event analysis, with the primary audience being graduate students pursuing a Masters degree in biostatistics or a PhD in one of the other departments at the Harvard Chan School. Covered in the course will be: an introduction to various types of censoring and truncation that commonly arise; the mathematical representations of time-to-event distributions, such as via the hazard and survivor functions; nonparametric methods such as Kaplan-Meier estimation of the survivor function and log-rank test for hypothesis testing; semi-parametric and parametric regression modeling techniques, such as the Cox model, the accelerated failure time model, the additive hazards model and cure fraction models; survival analysis within the causal inference paradigm; the analysis of competing and semi-competing risks; outcome-dependent sampling schemes, such as nested case-control and case-cohort designs; and, power/sample size calculations for studies with time-to-event endpoints. Throughout, equal emphasis will be given to the theoretical/technical underpinnings of survival analysis and to the use of real world data examples.
 

BST 231 - Statistical Inference I (HSPH: 5 credits, HMS: 4 credits)

Spring Semester Course

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.

BST 261 - Data Science II (HSPH: 2.5 credits, HMS: 2 credits)

Spring Semester Course

This course is an introduction to deep learning, a branch of machine learning concerned with the construction, development, and application of neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. We will cover a range of topics including basic neural networks, convolutional networks, and recurrent networks, and applications to problem domains like computer vision and speech recognition. Programming (Python) and case studies will be used throughout the course to provide hands-on training in these concepts.

EPI 293 - Analysis of Genetic Association Studies (HSPH: 2.5 credits, HMS: 2 credits)

January Term Course

At the end of this course students will grasp Concept and Theory, Methods and Software Tools needed to critically evaluate and conduct genetic and genomic association studies in unrelated individuals and family samples, including: basic molecular and population genetics, marker selection algorithms, haplotyping, multiple comparisons issues, population stratification, genome-wide association studies, genotype imputation, gene-gene and gene-environment interaction, analysis of microarray data (including gene expression, methylation data analysis, eQTL mapping), next-generation sequencing data analysis and genetics simulation studies. Useful software tools will be introduced and practiced in labs and projects. Students interested in methodology development will find interesting research topics to pursue further. Students interested in application will learn cutting-edge methods and tools for their ongoing projects. Course materials will be updated according to the fast-growing areas of genomics and other omics studies.
 

DPI 617 - Law, Order, and Algorithms (HKS: 4 credits, HMS: 4 credits)

Spring Semester Course

Data and algorithms are rapidly transforming law enforcement and the criminal legal system, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we examine the often subtle relationship between law, public policy, and technology, drawing on recent court decisions, and applying methods from economics, statistics, and machine learning. We survey the legal and ethical principles for assessing the equity of algorithms, describe computational techniques for designing fairer systems, and consider how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises, though prior programming experience is not necessary.

6.3900 - Introduction to Machine Learning (MIT: 12 units, HMS: 4 credits)

Fall and Spring Semesters

Introduction to the principles and algorithms of machine learning from an optimization perspective. Topics include linear and non-linear models for supervised, unsupervised, and reinforcement learning, with a focus on gradient-based methods and neural-network architectures. Previous experience with algorithms may be helpful.

7.75 - Human Genetics and Genomics (MIT: 12 units, HMS: 4 credits)

Spring Semester Course

Upper level seminar offering in-depth analysis and engaged discussion of primary literature on the dimensions and phenotypic consequences of variation in human genes, chromosomes, and genomes.  Topics include the human genome project; pedigree analysis; mutation and selection; linkage and association studies; medical genetics and disease; sex chromosomes and sex differences; the biology of the germ line; epigenetics, imprinting, and transgenerational inheritance; human origins; and evolutionary and population genetics. Students taking graduate version complete additional assignments. Limited to 20 total for versions meeting together.

HST 971 - Strategic Decision Making in Life Science Ventures (MIT: 9 units, HMS: 4 credits) Same course as 15.363, students are recommended to enroll in HST 971 listing. 

Spring Semester Course

Surveys key strategic decisions faced by managers, investors and scientists at each stage in the value chain of the life science industry. Aims to develop students' ability to understand and effectively assess these strategic challenges. Focuses on the biotech sector, with additional examples from the digital health and precision medicine industries. Includes case studies, analytical models, and detailed quantitative analysis. Intended for students interested in building a life science company or working in the sector as a manager, consultant, analyst, or investor. Provides analytical background to the industry for biological and biomedical scientists, engineers and physicians with an interest in understanding the commercial dynamics of the life sciences or the commercial potential of their research.

HST 978 - Healthcare Ventures (MIT: 12 units, HMS: 4 credits) Same course as 15.367, students are recommended to enroll in HST 978 listing. 

Spring Semester Course

Addresses healthcare entrepreneurship with an emphasis on startups bridging care re-design, digital health, medical devices, and new healthcare business models. Includes prominent speakers and experts from key domains across venture capital, medicine, pharma, med devices, regulatory, insurance, software, design thinking, entrepreneurship, including many alumni from the class sharing their journeys. Provides practical experiences in venture validation/creation through team-based work around themes. Illustrates best practices in identifying and validating health venture opportunities amid challenges of navigating healthcare complexity, team dynamics, and venture capital raising process. Intended for students from engineering, medicine, public health, and MBA programs.