diff --git a/_config.yml b/_config.yml index 9f370bb..c2712b1 100644 --- a/_config.yml +++ b/_config.yml @@ -29,7 +29,7 @@ heading_anchors: true permalink: pretty aux_links: Canvas: - - 'https://canvas.harvard.edu/courses/117878' + - 'https://canvas.harvard.edu/courses/134015' Harvard DBMI: - 'https://dbmi.hms.harvard.edu' Zitnik Lab: diff --git a/_modules/week-01.md b/_modules/week-01.md index 783d80f..74239ca 100644 --- a/_modules/week-01.md +++ b/_modules/week-01.md @@ -4,10 +4,10 @@ title: Week 1 Course overview and introduction to biomedical AI -Jan 23 +Jan 25 : **Lecture**{: .label .label-purple }[What makes biomedical problems unique](/BMI702/lectures/week01) : [Slides](/BMI702/assets/zitnik-BMI702-L1.pdf), [Reading List](/BMI702/lectures/week01) -Jan 24 -: **Quiz**{: .label .label-green }[Week 2 pre-class quiz](#) (due Jan 29) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Jan 26 +: **Quiz**{: .label .label-green }[Week 2 pre-class quiz](#) (due Feb 1) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-02.md b/_modules/week-02.md index 19276ce..7255509 100644 --- a/_modules/week-02.md +++ b/_modules/week-02.md @@ -4,10 +4,10 @@ title: Week 2 Clinical research using EHR data, subtype discovery, disease diagnosis and prognosis prediction -Jan 30 +Feb 1 : **Module 1**{: .label .label-blue }**Lecture**{: .label .label-purple }[Clinical AI Part I](/BMI702/lectures/module1/week02) - : [Slides](/BMI702/assets/zitnik-BMI702-L2.pdf), [Reading List](/BMI702/lectures/module1/week02) + : [Slides](#), [Reading List](/BMI702/lectures/module1/week02) -Jan 31 -: **Quiz**{: .label .label-green }[Week 3 pre-class quiz](#) (due Feb 5) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Feb 2 +: **Quiz**{: .label .label-green }[Week 3 pre-class quiz](#) (due Feb 8) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-03.md b/_modules/week-03.md index 2c15be6..5823e10 100644 --- a/_modules/week-03.md +++ b/_modules/week-03.md @@ -2,12 +2,12 @@ title: Week 3 --- -Multi-institutional EHR systems, transfer learning, federated learning, clinical workflows +Multi-institutional EHR, transfer learning, federated learning -Feb 6 +Feb 8 : **Module 1**{: .label .label-blue }**Lecture**{: .label .label-purple }[Clinical AI Part II](/BMI702/lectures/module1/week03) - : [Slides](/BMI702/assets/zitnik-BMI702-L3.pdf), [Reading List](/BMI702/lectures/module1/week03) + : [Slides](#), [Reading List](/BMI702/lectures/module1/week03) -Feb 7 -: **Quiz**{: .label .label-green }[Week 4 pre-class quiz](#) (due Feb 12) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Feb 9 +: **Quiz**{: .label .label-green }[Week 4 pre-class quiz](#) (due Feb 15) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-04.md b/_modules/week-04.md index 07dbf24..5f2682a 100644 --- a/_modules/week-04.md +++ b/_modules/week-04.md @@ -2,12 +2,12 @@ title: Week 4 --- -Interpretability and explainability in biomedical AI +Interpretability and explainability, bias and distribution shifts -Feb 13 -: **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy AI Part I](/BMI702/lectures/module2/week04) - : [Slides](/BMI702/assets/zitnik-BMI702-L4.pdf), [Reading List](/BMI702/lectures/module2/week04) +Feb 15 +: **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy & Efficient AI Part I](/BMI702/lectures/module2/week04) + : [Slides](#), [Reading List](/BMI702/lectures/module2/week04) -Feb 13 -: **Quiz**{: .label .label-green }[Week 5 pre-class quiz](#) (due Feb 26) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Feb 16 +: **Quiz**{: .label .label-green }[Week 5 pre-class quiz](#) (due Feb 22) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-05.md b/_modules/week-05.md index 675fe1b..bccb9ea 100644 --- a/_modules/week-05.md +++ b/_modules/week-05.md @@ -2,16 +2,16 @@ title: Week 5 --- -Bias and fairness in biomedical AI +Few-shot learning, scaling laws, generalization and robustness -Feb 27 -: **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy AI Part II](/BMI702/lectures/module2/week05) - : [Slides](/BMI702/assets/zitnik-BMI702-L5.pdf), [Reading List](/BMI702/lectures/module2/week05) +Feb 22 +: **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy & Efficient AI Part II](/BMI702/lectures/module2/week05) + : [Slides](#), [Reading List](/BMI702/lectures/module2/week05) -Feb 28 -: **Quiz**{: .label .label-green }[Week 6 pre-class quiz](#) (due Mar 5) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Feb 23 +: **Quiz**{: .label .label-green }[Week 6 pre-class quiz](#) (due Feb 29) + : [Canvas](https://canvas.harvard.edu/courses/134015) -Mar 1 -: **PSet released**{: .label .label-yellow }[PSet 1: Bias, explainability, and fairness](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) \ No newline at end of file +Feb 23 +: **PSet released**{: .label .label-yellow }[PSet 1: Bias, trustworthiness, and fairness](#) + : [Canvas](https://canvas.harvard.edu/courses/134015) \ No newline at end of file diff --git a/_modules/week-06.md b/_modules/week-06.md index 006f0e4..9ec5c1d 100644 --- a/_modules/week-06.md +++ b/_modules/week-06.md @@ -4,10 +4,10 @@ title: Week 6 Foundations of geometric deep learning, graph representation learning, link prediction, node classification, graph clustering, graph classification, semi-supervised learning, label propagation, network medicine, disease modules and endotypes -Mar 6 +Feb 29 : **Module 3**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical graph learning Part I](/BMI702/lectures/module3/week06) - : [Slides](/BMI702/assets/zitnik-BMI702-L6.pdf), [Reading List](/BMI702/lectures/module3/week06) + : [Slides](#), [Reading List](/BMI702/lectures/module3/week06) -Mar 7 -: **Quiz**{: .label .label-green }[Week 7 pre-class quiz](#) (due Mar 12) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Mar 1 +: **Quiz**{: .label .label-green }[Week 7 pre-class quiz](#) (due Mar 7) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-07.md b/_modules/week-07.md index 8184843..c761d0c 100644 --- a/_modules/week-07.md +++ b/_modules/week-07.md @@ -4,14 +4,14 @@ title: Week 7 Machine learning with heterogeneous graphs, multimodal learning, graph neural networks, knowledge graph embeddings, reasoning over knowledge graphs -Mar 20 +Mar 7 : **Module 3**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical graph learning Part II](/BMI702/lectures/module3/week07) - : [Slides - Part 1](/BMI702/assets/zitnik-BMI702-L7-Part-1.pdf), [Slides - Part 2](/BMI702/assets/li-BMI702-L7-Part-2.pdf), [Reading List](/BMI702/lectures/module3/week07) + : [Slides - Part 1](#), [Slides - Part 2](#), [Reading List](/BMI702/lectures/module3/week07) -Mar 21 -: **Quiz**{: .label .label-green }[Week 8 pre-class quiz](#) (due Mar 26) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Mar 8 +: **Quiz**{: .label .label-green }[Week 8 pre-class quiz](#) (due Mar 21) + : [Canvas](https://canvas.harvard.edu/courses/134015) -Mar 22 -: **PSet due**{: .label .label-yellow }[PSet 1: Bias, explainability, and fairness](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) \ No newline at end of file +Mar 8 +: **PSet due**{: .label .label-yellow }[PSet 1: Bias, trustworthiness, and fairness](#) + : [Canvas](https://canvas.harvard.edu/courses/134015) \ No newline at end of file diff --git a/_modules/week-08.md b/_modules/week-08.md index efa56cd..54016aa 100644 --- a/_modules/week-08.md +++ b/_modules/week-08.md @@ -4,14 +4,14 @@ title: Week 8 Foundations of natural language processing and understanding -Mar 27 +Mar 21 : **Module 4**{: .label .label-blue }**Lecture**{: .label .label-purple }[Medical language modeling Part I](/BMI702/lectures/module4/week08) - : [Slides](/BMI702/assets/li-BMI702-L8.pdf), [Reading List](/BMI702/lectures/module4/week08) + : [Slides](#), [Reading List](/BMI702/lectures/module4/week08) -Mar 28 -: **Quiz**{: .label .label-green }[Week 9 pre-class quiz](#) (due Apr 2) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Mar 22 +: **Quiz**{: .label .label-green }[Week 9 pre-class quiz](#) (due Mar 28) + : [Canvas](https://canvas.harvard.edu/courses/134015) -Mar 29 -: **PSet released**{: .label .label-yellow }[PSet 2: Biomedical networks and graph embeddings](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Mar 22 +: **PSet released**{: .label .label-yellow }[PSet 2: Knowledge graphs and geometric deep learning](#) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-09.md b/_modules/week-09.md index 475499d..fd3f769 100644 --- a/_modules/week-09.md +++ b/_modules/week-09.md @@ -4,10 +4,10 @@ title: Week 9 Clinical trial site identification, patient trial matching, clinical trial recruitment -Apr 3 +Mar 28 : **Module 4**{: .label .label-blue }**Lecture**{: .label .label-purple }[Medical language modeling Part II](/BMI702/lectures/module4/week09) - : [Slides](/BMI702/assets/zitnik-BMI702-L9.pdf), [Reading List](/BMI702/lectures/module4/week09) + : [Slides](#), [Reading List](/BMI702/lectures/module4/week09) -Apr 4 -: **Quiz**{: .label .label-green }[Week 10 pre-class quiz](#) (due Apr 9) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Apr 29 +: **Quiz**{: .label .label-green }[Week 10 pre-class quiz](#) (due Apr 4) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-10.md b/_modules/week-10.md index 3d898bb..8ae9f72 100644 --- a/_modules/week-10.md +++ b/_modules/week-10.md @@ -4,14 +4,14 @@ title: Week 10 Foundations of biomedical imaging, self-supervised learning, analysis of radiology images -Apr 10 +Apr 4 : **Module 5**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical imaging Part I](/BMI702/lectures/module5/week10) - : [Slides](/BMI702/assets/rajpurkar-BMI702-L10.pdf), [Reading List](/BMI702/lectures/module5/week10) + : [Slides](#), [Reading List](/BMI702/lectures/module5/week10) -Apr 11 -: **Quiz**{: .label .label-green }[Week 11 pre-class quiz](#) (due Apr 16) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Apr 5 +: **Quiz**{: .label .label-green }[Week 11 pre-class quiz](#) (due Apr 11) + : [Canvas](https://canvas.harvard.edu/courses/134015) -Apr 12 -: **PSet due**{: .label .label-yellow }[PSet 2: Biomedical networks and graph embeddings](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) \ No newline at end of file +Apr 5 +: **PSet due**{: .label .label-yellow }[PSet 2: Knowledge graphs and geometric deep learning](#) + : [Canvas](https://canvas.harvard.edu/courses/134015) \ No newline at end of file diff --git a/_modules/week-11.md b/_modules/week-11.md index 3f71ffb..68f6609 100644 --- a/_modules/week-11.md +++ b/_modules/week-11.md @@ -4,14 +4,14 @@ title: Week 11 Multimodal learning, analysis of histopathology slides, quantitative pathology in cancer diagnosis and prognosis -Apr 17 +Apr 11 : **Module 5**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical imaging Part II](/BMI702/lectures/module5/week11) - : [Slides](https://drive.google.com/file/d/1w6ED8dOF8DKFgFdFbmwPNYZRT9z2HwCY/view?usp=share_link), [Reading List](/BMI702/lectures/module5/week11) + : [Slides](#), [Reading List](/BMI702/lectures/module5/week11) -Apr 18 -: **Quiz**{: .label .label-green }[Week 12 pre-class quiz](#) (due Apr 23) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Apr 12 +: **Quiz**{: .label .label-green }[Week 12 pre-class quiz](#) (due Apr 18) + : [Canvas](https://canvas.harvard.edu/courses/134015) -Apr 19 +Apr 12 : **PSet released**{: .label .label-yellow }[PSet 3: Biomedical imaging methods and applications](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) \ No newline at end of file + : [Canvas](https://canvas.harvard.edu/courses/134015) \ No newline at end of file diff --git a/_modules/week-12.md b/_modules/week-12.md index b123068..d560f03 100644 --- a/_modules/week-12.md +++ b/_modules/week-12.md @@ -2,12 +2,12 @@ title: Week 12 --- -Overview of drug discovery and development, AI-guided drug design, small-molecule generation, molecule optimization, identification and characterization of therapeutic targets, high-throughput chemical and genetic perturbations +AI-guided drug design, small-molecule generation, molecule optimization, identification and characterization of therapeutic targets, design of chemical and genetic perturbations -Apr 24 -: **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Therapeutic science and drug discovery Part I](/BMI702/lectures/module6/week12) - : [Slides](/BMI702/assets/zitnik-BMI702-L12.pdf), [Reading List](/BMI702/lectures/module6/week12) +Apr 18 +: **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Generative AI Part I](/BMI702/lectures/module6/week12) + : [Slides](#), [Reading List](/BMI702/lectures/module6/week12) -Apr 25 -: **Quiz**{: .label .label-green }[Week 13 pre-class quiz](#) (due Apr 30) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Apr 19 +: **Quiz**{: .label .label-green }[Week 13 pre-class quiz](#) (due Apr 25) + : [Canvas](https://canvas.harvard.edu/courses/134015) diff --git a/_modules/week-13.md b/_modules/week-13.md index f9b1962..452ec22 100644 --- a/_modules/week-13.md +++ b/_modules/week-13.md @@ -2,16 +2,16 @@ title: Week 13 --- -Label-efficient learning, few-shot learning, biomarker discovery, indication inference, drug repurposing, adverse event prediction +Protein design and digital twins -May 1 -: **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Therapeutic science and drug discovery Part II](/BMI702/lectures/module6/week13) - : [Slides](/BMI702/assets/zitnik-BMI702-L13.pdf), [Reading List](/BMI702/lectures/module6/week13) +Apr 25 +: **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Generative AI Part II](/BMI702/lectures/module6/week13) + : [Slides](#), [Reading List](/BMI702/lectures/module6/week13) -May 2 -: **Quiz**{: .label .label-green }[Week 14 pre-class quiz](#) (due May 7) - : [Canvas](https://canvas.harvard.edu/courses/117878) +Apr 26 +: **Quiz**{: .label .label-green }[Week 14 pre-class quiz](#) (due May 2) + : [Canvas](https://canvas.harvard.edu/courses/134015) -May 3 +Apr 26 : **PSet due**{: .label .label-yellow }[PSet 3: Biomedical imaging methods and applications](#) - : [Canvas](https://canvas.harvard.edu/courses/117878) \ No newline at end of file + : [Canvas](https://canvas.harvard.edu/courses/134015) \ No newline at end of file diff --git a/_modules/week-14.md b/_modules/week-14.md index b9f4c32..332e4e7 100644 --- a/_modules/week-14.md +++ b/_modules/week-14.md @@ -4,7 +4,7 @@ title: Week 14 Introduction to ethical frameworks, data privacy, regulation and liability aspects of AI -May 8 +May 2 : **Lecture**{: .label .label-purple }[Ethical and legal considerations for biomedical AI ](/BMI702/lectures/week14) - : [Slides](/BMI702/assets/gerke-BMI702-L14.pdf), [Reading List](/BMI702/lectures/week14) + : [Slides](#), [Reading List](/BMI702/lectures/week14) diff --git a/_sass/color_schemes/harvardred.scss b/_sass/color_schemes/harvardred.scss index 138e4c8..1f1f859 100644 --- a/_sass/color_schemes/harvardred.scss +++ b/_sass/color_schemes/harvardred.scss @@ -7,7 +7,7 @@ $body-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helve // Layout $gutter-spacing: $sp-6; $gutter-spacing-sm: $sp-4; -$nav-width: 250px; +$nav-width: 300px; $content-width: 950px; // Components diff --git a/_schedules/weekly.md b/_schedules/weekly.md index 9e3a36e..7d57a36 100644 --- a/_schedules/weekly.md +++ b/_schedules/weekly.md @@ -23,32 +23,27 @@ timeline: schedule: - name: - name: Monday - events: - - name: Lecture - start: 1:00 PM - end: 3:00 PM - location: Armenise Modell 100A, 200 Longwood Ave - - name: OH Prof Zitnik - start: 3:00 PM - end: 4:00 PM - location: Countway 309, 10 Shattuck St - name: Tuesday events: - - name: OH Chen - start: 5:00 PM - end: 6:00 PM - location: Countway 423/424 Open area + - name: OH Ullanat + start: 3:00 PM + end: 4:00 PM + location: Countway 423/424 - name: Wednesday - name: Thursday events: - - name: OH Huang - start: 3:00 PM + - name: Lecture + start: 2:00 PM end: 4:00 PM - location: Countway 423/424 Open area + location: Countway 403 + - name: OH Prof Zitnik + start: 4:00 PM + end: 5:00 PM + location: Countway 309 - name: Friday events: - name: OH Ektefaie - start: 10:00 AM - end: 11:00 AM - location: Countway 423/424 Open area + start: 11:00 AM + end: 12:00 PM + location: Countway 423/424 --- diff --git a/_staffers/marinka.md b/_staffers/marinka.md index e80c908..f32ccb5 100644 --- a/_staffers/marinka.md +++ b/_staffers/marinka.md @@ -4,5 +4,5 @@ role: Instructor email: marinka@hms.harvard.edu website: https://zitniklab.hms.harvard.edu photo: marinka.png -office-hours: Mo, 3pm - 4pm +office-hours: Thu, 4pm - 5pm --- diff --git a/_staffers/richard.md b/_staffers/richard.md deleted file mode 100644 index 99fbf32..0000000 --- a/_staffers/richard.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -name: Richard Chen -role: Staff -email: richardchen@g.harvard.edu -website: http://richarizardd.me -photo: richard.png -office-hours: Tue, 5pm - 6pm ---- diff --git a/_staffers/varun.md b/_staffers/varun.md new file mode 100644 index 0000000..6249b76 --- /dev/null +++ b/_staffers/varun.md @@ -0,0 +1,8 @@ +--- +name: Varun Ullanat +role: Staff +email: vullanat@hms.harvard.edu +website: https://dbmi.hms.harvard.edu/people/varun-ullanat +photo: varun.png +office-hours: Tue, 3pm - 4pm +--- diff --git a/_staffers/yasha.md b/_staffers/yasha.md index 71197b0..78d5589 100644 --- a/_staffers/yasha.md +++ b/_staffers/yasha.md @@ -4,5 +4,5 @@ role: Staff email: yasha_ektefaie@g.harvard.edu website: https://www.yashaektefaie.com photo: yasha.png -office-hours: Fri, 10am - 11am +office-hours: Fri, 11am - 12pm --- diff --git a/_staffers/yepeng.md b/_staffers/yepeng.md deleted file mode 100644 index 9d49b6a..0000000 --- a/_staffers/yepeng.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -name: Yepeng Huang -role: Staff -email: yepenghuang@hsph.harvard.edu -website: https://www.linkedin.com/in/yepeng-huang -photo: yepeng.png -office-hours: Thu, 3pm - 4pm ---- diff --git a/assets/images/richard.png b/assets/images/richard.png deleted file mode 100644 index 3313c4e..0000000 Binary files a/assets/images/richard.png and /dev/null differ diff --git a/assets/images/varun.png b/assets/images/varun.png new file mode 100644 index 0000000..8305ef1 Binary files /dev/null and b/assets/images/varun.png differ diff --git a/assets/images/yepeng.png b/assets/images/yepeng.png deleted file mode 100644 index 8c64f10..0000000 Binary files a/assets/images/yepeng.png and /dev/null differ diff --git a/assets/zitnik-BMI702-L1.pdf b/assets/zitnik-BMI702-L1.pdf index 4553f9c..126c224 100644 Binary files a/assets/zitnik-BMI702-L1.pdf and b/assets/zitnik-BMI702-L1.pdf differ diff --git a/index.md b/index.md index b7f2910..18a4cb3 100644 --- a/index.md +++ b/index.md @@ -9,15 +9,15 @@ description: BMI 702 - Biomedical Artificial Intelligence # [BMI 702](https://dbmi.hms.harvard.edu/education/courses/bmi-702) | Biomedical Artificial Intelligence {: .mb-2 } -Harvard - Foundations of Biomedical Informatics II, Spring 2023 +Harvard - Foundations of Biomedical Informatics II, Spring 2024 {: .mb-0 .fs-6 .text-grey-dk-000 }

- Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This introductory course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine. + Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine.

- It outlines foundational algorithms and emphasizes the subtleties of working with biomedical data and ways to evaluate and transition machine learning methods into biomedical and clinical implementation. An important consideration in this course is the broader impact of artificial intelligence, particularly topics of bias and fairness, interpretability, and ethical and legal considerations when dealing with artificial intelligence. + The curriculum delves into foundational algorithms and highlights the nuances of handling biomedical data. It places a strong emphasis on strategies for evaluating and seamlessly integrating machine learning methods into biomedical research and clinical practice. A key aspect of this course is its focus on the broader implications of artificial intelligence. This includes critical discussions on topics such as trustworthiness, interpretability, evaluation, and the ethical and legal challenges associated with the implementation of artificial intelligence in healthcare.

diff --git a/lectures/module2/index.md b/lectures/module2/index.md index 8c78b72..8b64bd6 100644 --- a/lectures/module2/index.md +++ b/lectures/module2/index.md @@ -1,6 +1,6 @@ --- layout: default -title: M2 - Trustworthy AI +title: M2 - Trustworthy & Efficient AI nav_order: 5 has_children: true --- diff --git a/lectures/module2/week04.md b/lectures/module2/week04.md index 961fe4f..2d4e2dd 100644 --- a/lectures/module2/week04.md +++ b/lectures/module2/week04.md @@ -1,12 +1,12 @@ --- layout: page -title: Trustworthy AI Part I -parent: M2 - Trustworthy AI +title: Trustworthy & Efficient AI Part I +parent: M2 - Trustworthy & Efficient AI nav_order: 1 description: BMI 702 - Foundations in Biomedical Informatics II - Biomedical Artificial Intelligence --- -# Week 4 - Trustworthy AI Part I +# Week 4 - Trustworthy & Efficient AI Part I ## Required Reading (Week 4 Pre-Class Quiz) diff --git a/lectures/module2/week05.md b/lectures/module2/week05.md index d0c0600..099fb11 100644 --- a/lectures/module2/week05.md +++ b/lectures/module2/week05.md @@ -1,12 +1,12 @@ --- layout: page -title: Trustworthy AI Part II -parent: M2 - Trustworthy AI +title: Trustworthy & Efficient AI Part II +parent: M2 - Trustworthy & Efficient AI nav_order: 2 description: BMI 702 - Foundations in Biomedical Informatics II - Biomedical Artificial Intelligence --- -# Week 5 - Trustworthy AI Part II +# Week 5 - Trustworthy & Efficient AI Part II ## Required Reading (Week 5 Pre-Class Quiz) diff --git a/lectures/module6/index.md b/lectures/module6/index.md index 6e735ae..6e9ed66 100644 --- a/lectures/module6/index.md +++ b/lectures/module6/index.md @@ -1,6 +1,6 @@ --- layout: default -title: M6 - Therapeutic Science +title: M6 - Generative AI nav_order: 9 has_children: true --- diff --git a/lectures/module6/week12.md b/lectures/module6/week12.md index a49c4b4..7a550aa 100644 --- a/lectures/module6/week12.md +++ b/lectures/module6/week12.md @@ -1,12 +1,12 @@ --- layout: page -title: Therapeutic Science Part I +title: Generative AI Part I nav_order: 1 -parent: M6 - Therapeutic Science +parent: M6 - Generative AI description: BMI 702 - Foundations in Biomedical Informatics II - Biomedical Artificial Intelligence --- -# Week 12 - Therapeutic Science Part I +# Week 12 - Generative AI Part I ## Required Reading (Week 12 Pre-Class Quiz) diff --git a/lectures/module6/week13.md b/lectures/module6/week13.md index 0003413..3cf6222 100644 --- a/lectures/module6/week13.md +++ b/lectures/module6/week13.md @@ -1,12 +1,12 @@ --- layout: page -title: Therapeutic Science Part II +title: Generative AI Part II nav_order: 2 -parent: M6 - Therapeutic Science +parent: M6 - Generative AI description: BMI 702 - Foundations in Biomedical Informatics II - Biomedical Artificial Intelligence --- -# Week 13 - Therapeutic Science Part II +# Week 13 - Generative AI Part II ## Required Reading (Week 13 Pre-Class Quiz) diff --git a/syllabus.md b/syllabus.md index 9c12211..33735e1 100644 --- a/syllabus.md +++ b/syllabus.md @@ -21,7 +21,7 @@ Artificial intelligence is poised to enable breakthroughs in science and reshape ### Goals {:.no_toc} -- Prepare students for advanced courses in data science, machine learning, and statistics, by providing the necessary foundation and context +- Prepare students for advanced courses in data science, machine learning, and statistics by providing the necessary foundation and context - Empower students to apply computational and inferential thinking to address real-world problems - Understand artificial intelligence methods from a practical perspective - Understand best practices in implementing, evaluating, and validating ML methods on biomedical data @@ -34,15 +34,15 @@ Artificial intelligence is poised to enable breakthroughs in science and reshape ### Syllabus {:.no_toc} -The overall structure is as follows. The course comprises 14 weeks. We provide a course overview and introduction to biomedical AI in the first week. The remaining 12 weeks are divided into six modules. The first week in each module is foundational and introduces key machine learning concepts in the area, and the following week covers advanced topics and frontiers of the same area. The final week of the course is introduces student to ethical and legal considerations for biomedical AI. +The overall structure is as follows. The course comprises 14 weeks. We provide a course overview and introduction to biomedical AI in the first week. The remaining 12 weeks are divided into six modules. The first week in each module is foundational and introduces key machine learning concepts in the area, and the following week covers advanced topics and frontiers of the same area. The final week of the course introduces students to ethical and legal considerations for biomedical AI. ## Assigments and Grading -* There are three problem sets in the course. Assignments are released on Wednesday at 9:00am EST and are due at 11:59pm EST on Wednesday (14 days after they are released). Submissions must be made through Canvas. -* Pre-class quizzes open at 9:00am EST on Tuesday and are due at 11:59pm EST on Sunday (pre-class quizzes close before lectures on Monday). +* There are three problem sets in the course. Assignments are released on Fridays at 9:00am EST and are due at 11:59pm EST on Friday (14 days after they are released). Submissions must be made through Canvas. +* Pre-class quizzes open at 9:00am EST on Friday and are due at 2:00pm EST on Thursday (pre-class quizzes close before lectures on Thursday). Quizzes must be completed in Canvas. -*Delayed beyond 24 hours of deadline: no credit. In the case of illness/absence, please contact the course instructor. We will work with you to make up any missed assignments.* +*Delayed beyond 24 hours of deadline: no credit. In the case of illness/absence, contact the course instructor. We will work with you to make up any missed assignments.* Delayed beyond 24 hours of deadline: no credit @@ -127,17 +127,44 @@ Regrade requests will **not** be considered in cases in which: ## Policies -### Late Policy +### We Want You to Succeed! {:.no_toc} -All assignments are due at 11:59 pm on the due date specified on the syllabus. +You are more than welcome to visit our office hours and talk with us. We know graduate school can be stressful and we want to help you succeed. + +### Late Policy +{:.no_toc} Extensions are only provided in the case of exceptional circumstances. For that, email the course instructor to request an extension. If you make a request close to the deadline, we can not guarantee that you will receive a response before the deadline. Additionally, simply submitting a request does not guarantee you will receive an extension. Even if your work is incomplete, please submit before the deadline so you can receive credit for the work you did complete. +### Assignments +{:.no_toc} + +Data science is a collaborative activity. While you may talk with others about the homework, we ask that you write your solutions individually in your own words. If we suspect that you have submitted plagiarized work, we will call you in for a meeting. If we then determine that plagiarism has occurred, we reserve the right to give you a negative full score (-100%) or lower on the assignments in question, along with reporting your offense to the Center of Student Conduct. + +Rather than copying someone else's work, ask for help. You are not alone in this course! The entire staff is here to help you succeed. If you invest the time to learn the material and complete the assignments, you won't need to copy any answers. + +### Using Large Language Models (LLMs) and Generative AI +{:.no_toc} + +The following policy outlines the guidelines for the use of generative AI and LLMs in student assignments. + +* Responsibility for content: Students who use LLMs and generative AI tools in their assignments must take full responsibility for the content they submit. This includes ensuring the accuracy, relevance, and originality of the information provided by these tools. + +* Acknowledgment of AI use: Students must clearly acknowledge any use of LLMs or generative AI in their assignments. This acknowledgment should specify the nature and extent of the assistance received from these tools. LLMs and generative AI can be used to enhance the educational experience, and help with ideation and understanding of complex concepts. However, students must perform the critical thinking, analysis, and synthesis of information. + +* Ethical use and originality: Students must use these tools ethically, following the principles of academic honesty. The use of AI to plagiarize, misrepresent original work, or fabricate data is strictly prohibited. Students are encouraged to use these tools to inspire and inform their work, not to undermine the learning process. + +* Instructor discretion: Instructors may specify assignments where LLMs and generative AI use is particularly encouraged or prohibited, depending on the assignment's learning objectives. + +This policy helps students get ready for a future with AI in jobs and makes sure their education focuses on honesty and learning. [Students are encouraged to read this NEJM AI editorial on why we support the use of LLMs and generative AI in BMI 702.](https://ai.nejm.org/doi/full/10.1056/AIe2300128) + ### Collaboration Policy and Academic Dishonesty {:.no_toc} -We will be following the [Harvard Medical School policy on Academic Honesty](https://medstudenthandbook.hms.harvard.edu/4-student-conduct-and-responsibility), which states that using work or resources that are not your own or not permitted by the course may lead to disciplinary actions, up to and including a failing grade in the course. It is the student’s responsibility to be aware of these policies and ensure that their work adheres to them in detail and in spirit. Unless otherwise specified by the instructor, the assumption is that all work submitted must reflect the student’s own effort and understanding. Students are expected to clearly distinguish their own ideas and knowledge from information derived from other sources, including from collaboration with other people. Specifically, this means that: +All work in this course is governed by [Harvard Medical School’s academic integrity policies](https://issuu.com/hmsgraduateeducation/docs/handbook_updates_all_22-23_gc?fr=sYjRlNzYxOTI5MDQ). It is the students’ responsibility to be aware of these policies and to ensure that their work adheres to them both in detail and in spirit. Unless otherwise specified by the instructor, the assumption is that all work submitted must reflect the student’s own effort and understanding. Students are expected to clearly distinguish their own ideas and knowledge from information derived from other sources, including from conversations with other people. When working with others you must do so in the spirit of collaboration, not via a unidirectional transfer of information. Note that sharing or sending completed assignments to others will nearly always violate this collaborative standard. If you have a question about how best to complete an assignment in light of these policies, ask the instructor for clarification. + +Students are expected to clearly distinguish their own ideas and knowledge from information derived from other sources, including from collaboration with other people. Specifically, this means that: - Students must properly cite all submitted work appropriately. - Unless noted otherwise, students are expected to complete assignments, quizzes, and projects individually, not as teams. Discussion about course content and materials is acceptable, but sharing solutions is not acceptable. @@ -145,30 +172,34 @@ We will be following the [Harvard Medical School policy on Academic Honesty](htt If you have a question about how best to complete an assignment in light of these policies, ask the instructor for clarification. -### Assignments +### Community Standards {:.no_toc} -Data science is a collaborative activity. While you may talk with others about the homework, we ask that you write your solutions individually in your own words. If we suspect that you have submitted plagiarized work, we will call you in for a meeting. If we then determine that plagiarism has occurred, we reserve the right to give you a negative full score (-100%) or lower on the assignments in question, along with reporting your offense to the Center of Student Conduct. +Harvard Medical School is committed to supporting inclusive learning environments that value and affirm the diverse ideas and unique life experiences of all people. An equitable, inclusive classroom is a shared responsibility of both instructors and students, and both are encouraged to consider how their own experiences and biases may influence the learning environment. This requires an open mind and respect for differences of all kinds. -Rather than copying someone else's work, ask for help. You are not alone in this course! The entire staff is here to help you succeed. If you invest the time to learn the material and complete the assignments, you won't need to copy any answers. +Students are encouraged to contact the course director if they are experiencing bias or feel that their learning experience – including a course’s content, manner of instruction, or learning environment – is not inclusive. Program administrators and directors, the Office for Gender Equity, and the [Ombuds Office](https://hms.harvard.edu/departments/ombuds-office) are also available to discuss your experiences and provide support. Additionally, students can utilize [Harvard’s Anonymous Reporting Hotline](https://reportinghotline.harvard.edu/) to report issues related to bias. -### We want you to succeed! +### Academic and Other Support Services {:.no_toc} -If you are feeling overwhelmed, visit our office hours and talk with us. We know graduate school can be stressful and we want to help you succeed. +We value your well-being and recognize that as a graduate student you are asked to balance a variety of responsibilities and potential stressors: in class, in lab, and in life. If you are struggling with experiences either in- or outside of class, there are resources available to help. In addition to program leadership, master’s students can contact Kimberly_Lincoln@hms.harvard.edu, HMS Director of Administration and Student Affairs for Master’s Programs and Johanna_Gutlerner@hms.harvard.edu, Senior Associate Dean for Graduate Education, for support. + +### Wellbeing and Mental Health Services +{:.no_toc} -## Academic and Other Support Services +Counseling and Mental Health Services (CAMHS) is a counseling and mental health support service that seeks to work collaboratively with students and the University to support individuals experiencing some measure of distress in their lives. It provides coverage to students year-round and is included in the student health fee, regardless of insurance, at no additional cost. More information is available on the [CAMHS website](https://camhs.huhs.harvard.edu/) or by calling the main office at 617-495-2042. Urgent care can be reached 24/7 at 617-495-5711. -We value your well-being and recognize that as a graduate or professional student, you are asked to balance various responsibilities and potential stressors. If you are struggling with experiences either in or outside of class, there are resources available to help. HMS students should contact their home program administrator or the Office of Graduate Education to access academic or personal support services. +[CAMHS Care Line:](https://camhs.huhs.harvard.edu/camhs-cares) The CAMHS Cares line 617-495-2042 is a 24/7 support line available to Harvard students who have mental health concerns, whether you are in immediate distress or not, on-campus or elsewhere. This the Line can also be used as resource for Harvard personnel who needs advice about a student who may be experiencing a mental health crisis. At all times, including evenings, weekends, and holidays, you can follow the prompts to speak directly with a CAMHS Cares Counselor about an urgent concern or if you just need to talk to someone about a difficult challenge. -All students have access to Counseling and Mental Health Services (CAMHS) available in Longwood, Cambridge, or remotely via webcam or phone. The use of CAMHS is included in the student health fee, regardless of insurance, at no additional cost. More information is available at [https://camhs.huhs.harvard.edu](https://camhs.huhs.harvard.edu) or by calling the main office at 617-495-2042. Urgent care can be reached 24/7 at 617-495-5711. +[TimelyCare](https://camhs.huhs.harvard.edu/timelycare), a virtual mental health and wellbeing platform for all Harvard students covered by the Student Health Fee, offers free virtual mental health care including scheduled counseling, psychiatry, and self-care content to support wellbeing and mental health any time. Scheduled therapy appointments are readily available. ### Reasonable Accommodations {:.no_toc} -As an institution that values diversity and inclusion, our goal is to create learning environments that are usable, equitable, inclusive, and welcoming. Harvard University complies with federal legislation for individuals with disabilities and offers reasonable accommodations to qualified students with documented disabilities and temporary impairments. To make a request for reasonable accommodations in a course, students must first connect with their local disability office. The primary point of contact for HMS-based master’s and medical students is the HMS Director of Disability Services. - -Accommodations are determined through an interactive process and are not retroactive. Therefore, students should contact their local disability office as soon as possible, preferably at least two weeks before accommodations are needed in a course. Students are strongly encouraged to discuss their access needs with their instructors; however, instructors cannot independently institute individual accommodations without prior approval from the disability office. Student privacy surrounding disability status is recognized under FERPA. Information about accommodations is shared on a need-to-know basis with only those involved in instituting the accommodation. +As an institution that values diversity and inclusion, our goal is to create learning environments that are usable, equitable, inclusive and welcoming. Harvard University complies with federal legislation for individuals with disabilities and offers reasonable accommodations to qualified students with documented disabilities and temporary impairments. To make a request for reasonable accommodations in a course, students must first connect with their local disability office. The HMS Director of Disability Services, Timothy Rogers (timothy_rogers@hms.harvard.edu), is the point of contact for accommodation information for HMS master’s and MD students. + +Accommodations are determined through an interactive process and are not retroactive. Therefore, students should contact their local disability office as soon as possible, preferably at least two weeks before accommodations are needed in a course, or immediately following an injury or illness, in order to initiate the accommodation process. Students are strongly encouraged to discuss their access needs with their instructors; however, instructors cannot independently institute individual accommodations without prior approval from the disability office. Student privacy surrounding disability status is recognized under FERPA. Information about accommodations is shared on a need-to-know basis, and with only those individuals involved in instituting the accommodation. +