What is a lecture summary?
A lecture summary is a quick, organized way to capture the most important points from a lecture. It’s designed to help you and your peers review big ideas, key terms, and examples for exams and future projects. To make this easier, you’ll work in small groups, with each group responsible for summarizing a lecture.
Each lecture may have one or several modules. For a single-module lecture, your group will write one summary; for multiple modules, you’ll write one summary per module. Since lectures are about the same length, each group will handle a similar amount of content.
Note: No need to create your own document from scratch! We’ve set up a template Google Doc for each lecture summary, which you’ll find in the next section. Just open the template for your assigned lecture and start filling it in.
Due: One week after the lecture.
How to write a lecture summary?
Ready to put together a lecture summary? It’s simpler than it seems! Just follow the structure we’ve provided to make sure you cover the essentials—like key terms, main topics, and any references or comments. This approach helps you capture the core ideas in a way that’s easy to review later.
Click here to access the guide for your lecture summary. It walks you through each part of the summary step-by-step, so you know exactly what to include. Happy summarizing!
Lecture summaries
| Lecture Date | Topic | LInk to Summary | Due Date |
|---|---|---|---|
| 1/13 | Introduction | 23i1 | 1/20 |
| 1/15 | DATA - Proteomics I | ||
| 1/22 | DATA - Proteomics II | ||
| 1/27 | DATA - Genomics I | ||
| 1/29 | DATA - Genomics II | ||
| 2/3 | DATA - Knowledge Representation & Databases | ||
| 2/5 | MINING - Personal Genomes + Seq. Comparison + Multi-seq Alignment | ||
| 2/10 | MINING - Fast Alignment + Variant Calling (incl. a focused section on SVs) | ||
| 2/12 | MINING - Basic Multi-Omics + Supervised Mining #1 | ||
| 2/17 | Quiz on 1st Half | ||
| 2/19 | MINING - Supervised Mining #2 + Deep Learning Fundamentals #1 | ||
| 2/24 | MINING - Deep Learning Fundamentals #2 + Unsupervised Mining #1 | ||
| 2/26 | MINING - Unsupervised Mining #2 + Single-Cell Analysis #1 | ||
| 3/3 | MINING - Single-Cell Analysis #2 + Biomedical Image Analysis | ||
| 3/5 | MINING - Network Analysis | ||
| 3/24 | MINING - Privacy | ||
| 3/26 | MINING/MODELING - Deep Learning Advanced I | ||
| 3/31 | MINING/MODELING - Deep Learning Advanced II | ||
| 4/2 | SIMULATION - Protein Simulation I | ||
| 4/7 | SIMULATION - Protein Simulation II | ||
| 4/9 | SIMULATION - Protein Simulation III | ||
| 4/14 | SIMULATION - Protein Simulation IV | ||
| 4/16 | SIMULATION - Protein Simulation V | ||
| 4/21 | Quiz on 2nd Half |
Rubric
| Category | Description | Points | Points |
|---|---|---|---|
| Completeness (2 points) | Includes all required sections: Lecture Title, Objectives, Key Concepts, Main Content, References, Contributors. | All sections are present and properly filled out. | 2 |
| Missing or incomplete sections. | 1 | ||
| Major sections are missing. | 0 | ||
| Clarity and Organization (2 points) |
Summary is clear, concise, and well-organized, following the outlined structure. | Ideas are logically presented, and the structure is easy to follow | 2 |
| Minor issues with clarity or organization | 1 | ||
| Poorly organized or hard to understand. | 0 | ||
| Accuracy (3 points) |
Accurately reflects the lecture content with no major errors in key concepts, definitions, or topics. | Information is accurate and complete. | 3 |
| Minor inaccuracies or missing details. | 1-2 | ||
| Significant errors or misrepresentation of content. | 0 | ||
| References (1 points) |
Relevant references (ISL/ESL or additional readings) are included and properly cited. | References are appropriate and complete. | 1 |
| References are missing or irrelevant. | 0 | ||
| Contribution and Collaboration (1 points) |
All group members are listed as contributors, with evidence of collaboration. | Contributors are listed, and collaboration is evident. | 1 |
| Contributors are not listed, or no evidence of collaboration. | 0 | ||
| Discussion/Comments (1 points) |
Includes thoughtful discussion points or questions related to the lecture content. | Meaningful comments or discussion points are included. | 1 |
| Comments are missing or irrelevant. | 0 | ||
| Total | 10 |