Course website (Jekyll site, served via GitHub Pages) for UVA CS's Fall 2026 Machine Learning / Deep Learning course, taught by Prof. Yanjun Qi.
Live site: https://qiyanjun.github.io/2026Fall-UVA-CS-MachineLearningDeep/
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├── _config.yml # Jekyll site config (title, baseurl, permalinks, collections)
├── Gemfile # Ruby/Jekyll dependencies (github-pages gem)
├── index.html # Home page: renders the lecture schedule table
├── About.md # "Syllabus" nav page: course info, prerequisites, staff, policies
├── Assignments.md # "Assignments" nav page: HWs, quizzes, grading breakdown
├── LecturesByDate.md # "All-In-OnePage" nav page: every lecture's content in one long page
├── LecturesByTags.md # "Topics-as-Tables" nav page: lectures grouped/indexed by topic tag
├── z26fBlog.md # "Annoucements" nav page: dated course announcements/updates
├── 404.html # GitHub Pages 404 error page
├── atom.xml # Auto-generated RSS/Atom feed of lecture posts
├── LICENSE
│
├── _contents/ # One .md file per lecture (the "posts" collection) — see Module Structure below
├── _layouts/ # Jekyll page templates
│ ├── default.html # base HTML wrapper (loads sidebar/head, used by all pages)
│ ├── page.html # layout for standalone nav pages (About, Assignments, ...)
│ └── post.html # layout for a single lecture page under _contents/
├── _includes/ # Reusable HTML fragments
│ ├── head.html # <head> block (meta tags, CSS)
│ └── sidebar.html # left nav sidebar; auto-lists any page with `layout: page`
│
├── Lectures/ # Static assets: lecture PDFs (slides) linked from _contents/*.md
├── notebook/ # Jupyter notebooks and quiz/review PDFs used as lecture resources
└── public/ # Site assets: CSS, logo, favicon, images
Each file in _contents/ is a Jekyll collection item with YAML front matter (title, lecture, video, notes, categories, tags, lectureVersion, ...) that index.html, LecturesByDate.md, and LecturesByTags.md read to build the schedule/index tables. lecture: <name> points to a PDF of the same name in Lectures/.
Lectures are organized into 7 sections (S0–S6), reflected in the _contents/ filename prefixes (e.g. S2-L04-CNN.md). Each section starts with a S<N>-00Start.md overview file.
| Section | Theme | # Lectures |
|---|---|---|
| S0 | Introduction & math prerequisites (algebra/calculus review) | 2 |
| S1 | Basics of Supervised Learning on Tabular Data (linear/regularized regression, kNN, model selection, bias-variance) | 11 |
| S2 | Deep Learning on 2D Grid Data / Imaging (MLE, logistic regression, NN, CNN, PyTorch/Keras/HuggingFace, PCA) | 9 |
| S3 | Deep Learning on 1D Sequence Data / Language (text NNs, generative & naive Bayes classification, recent DL/LLM survey) | 7 |
| S4 | More Advanced Supervised Learning on Tabular Data (SVM + kernels + duality, decision trees, bagging, boosting) | 8 |
| S5 | Unsupervised Learning (hierarchical & k-means clustering, GMM/EM, reinforcement learning) | 7 |
| S6 | Wrap-up (review, final exam, final project) | 4 |
| Link | Purpose |
|---|---|
| Live course site | Main entry point — lecture-by-lecture schedule table with dates, slides, videos, and notes |
Syllabus (About.md) |
Course description, learning goals, prerequisites, instructor/TA contacts & office hours, grading policy |
Assignments (Assignments.md) |
HW list with out/in dates and weights, quiz schedule, midterm/final exam info, grading breakdown |
All-In-OnePage (LecturesByDate.md) |
Every lecture's full content rendered on a single scrollable page, in schedule order |
Topics-as-Tables (LecturesByTags.md) |
Lectures indexed/grouped by topic tag, for topic-based lookup instead of date order |
Annoucements (z26fBlog.md) |
Dated log of course announcements and updates (also mirrored in Canvas) |
| GitHub repo | Source of this site; Lectures/ (slide PDFs) and notebook/ (code notebooks, quiz/review PDFs) are browsable directly here |
| UVA Academic Calendar | Official university dates (drop deadlines, holidays, exam period) referenced on the home page |