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 |