Teach Me

# MIT OpenCourseWare — Knowledge Source Reference

**MIT OCW** (https://ocw.mit.edu) is a critical knowledge source — 2,500+ courses
with lecture notes, video lectures, problem sets, and exams from MIT professors.
All content is Creative Commons licensed and free.

## When to Search OCW

Search OCW when the topic is:
- Computer science fundamentals (algorithms, data structures, OS, networks)
- Mathematics (linear algebra, calculus, probability, discrete math)
- Engineering (signals, controls, circuits, systems design)
- AI/ML (machine learning, deep learning, NLP, computer vision)
- Physics, chemistry, biology fundamentals
- Economics, finance fundamentals

Do NOT search OCW for:
- Specific frameworks/libraries (React, Playwright, Django) — use official docs
- DevOps tools (Docker, K8s, CI/CD) — use official docs + tutorials
- Current industry practices — OCW is academic, may lag industry trends

## How to Search OCW

```
Search pattern: web_search "site:ocw.mit.edu [topic]"

Examples:
  "site:ocw.mit.edu dynamic programming"
  "site:ocw.mit.edu machine learning"
  "site:ocw.mit.edu operating systems"
  "site:ocw.mit.edu linear algebra"
  "site:ocw.mit.edu computer networks"
  "site:ocw.mit.edu distributed systems"
  "site:ocw.mit.edu database systems"
```

## OCW URL Patterns

```
Course page:    https://ocw.mit.edu/courses/[DEPT-NUMBER-TITLE-TERM]/
Lecture notes:  https://ocw.mit.edu/courses/[...]/resources/[lecture-file]/
Lecture videos: https://ocw.mit.edu/courses/[...]/resources/[lecture-video]/
Syllabus:       https://ocw.mit.edu/courses/[...]/pages/syllabus/
```

## Key Courses Reference

These are the highest-value OCW courses by topic area:

### Computer Science

| Course | Title | Level | Best For |
|--------|-------|-------|----------|
| 6.006 | Introduction to Algorithms | Undergrad | Sorting, searching, graphs, DP |
| 6.046J | Design and Analysis of Algorithms | Undergrad+ | Divide-conquer, greedy, NP |
| 6.851 | Advanced Data Structures | Grad | B-trees, hashing, cache-oblivious |
| 6.034 | Artificial Intelligence | Undergrad | Search, logic, learning basics |
| 6.036 | Introduction to Machine Learning | Undergrad | ML fundamentals, neural nets |
| 6.824 | Distributed Systems | Grad | MapReduce, Raft, distributed DBs |
| 6.828 | Operating System Engineering | Grad | Kernel, processes, virtual memory |
| 6.858 | Computer Systems Security | Grad | Buffer overflow, web security |
| 6.001 / 6.0001 | Structure and Interpretation / Intro to CS | Undergrad | Programming fundamentals |
| 6.031 | Software Construction | Undergrad | Testing, specs, concurrency |
| 6.172 | Performance Engineering | Undergrad | C optimization, profiling, SIMD |

### Mathematics

| Course | Title | Level | Best For |
|--------|-------|-------|----------|
| 18.06 | Linear Algebra | Undergrad | Vectors, matrices, eigenvalues (Strang!) |
| 18.065 | Matrix Methods in Data Analysis | Grad | ML applications of linear algebra |
| 18.01 | Single Variable Calculus | Undergrad | Derivatives, integrals |
| 18.02 | Multivariable Calculus | Undergrad | Partial derivatives, multiple integrals |
| 18.05 | Introduction to Probability and Statistics | Undergrad | Bayesian, frequentist |
| 6.042J | Mathematics for Computer Science | Undergrad | Proofs, combinatorics, probability |
| 18.650 | Statistics for Applications | Grad | Hypothesis testing, regression |

### AI / Machine Learning

| Course | Title | Level | Best For |
|--------|-------|-------|----------|
| 6.S191 | Introduction to Deep Learning | Undergrad | Neural nets, CNNs, RNNs, GANs |
| 6.867 | Machine Learning | Grad | Theory + practice |
| 6.864 | Advanced Natural Language Processing | Grad | NLP, transformers |
| MAS.S60 | How to AI (Almost) Anything | Grad | Multimodal AI, foundation models |

### Physics

| Course | Title | Level | Best For |
|--------|-------|-------|----------|
| 8.01 | Classical Mechanics | Undergrad | Newton's laws, energy, momentum, rotation |
| 8.02 | Electricity & Magnetism | Undergrad | E&M, circuits, Maxwell's equations |
| 8.03 | Vibrations and Waves | Undergrad | Wave mechanics — PREREQUISITE for quantum |
| 8.04 | Quantum Physics I (Zwiebach) | Undergrad | Wave mechanics, Schrödinger, superposition |
| 8.05 | Quantum Physics II | Undergrad | Formalism, angular momentum, spin |
| 8.06 | Quantum Physics III | Undergrad | Perturbation theory, scattering |
| 8.033 | Relativity | Undergrad | Special relativity, Lorentz transforms |
| 8.044 | Statistical Physics I | Undergrad | Thermodynamics, statistical mechanics |
| 8.962 | General Relativity | Grad | Einstein field equations, black holes |
| 8.421 | Atomic and Optical Physics I | Grad | Quantum optics, laser physics |

**Notable lecturers**: Walter Lewin (8.01, 8.02 — legendary demos), Barton Zwiebach
(8.04, 8.05, 8.06 — authored "Mastering Quantum Mechanics"), Allan Adams (8.04 2013 —
energetic, great for engagement).

### Engineering

| Course | Title | Level | Best For |
|--------|-------|-------|----------|
| 6.002 | Circuits and Electronics | Undergrad | Circuit analysis, op-amps |
| 6.003 | Signals and Systems | Undergrad | Fourier, Laplace, frequency domain |
| 2.007 | Design and Manufacturing I | Undergrad | Mechanical design, prototyping |
| 16.06 | Principles of Automatic Control | Undergrad | Control systems, feedback |

## What to Extract from OCW

When you find a relevant OCW course:

### 1. SYLLABUS (read first)
- **Topic ordering**: MIT professors have refined this over decades of teaching.
  Their progression is almost always pedagogically superior to random blog tutorials.
- **Prerequisites listed**: Tells you what the learner needs before starting.
- **Grading breakdown**: Shows which skills MIT considers most important.

### 2. LECTURE NOTES
- MIT's explanations are rigorous but clear.
- Use their **examples as inspiration** — rephrase in your own words, don't copy.
- Note their **progressive complexity order** — the sequence matters.
- Some courses have typed notes (better), others have student-scribed notes (variable quality).

### 3. PROBLEM SETS
- MIT problem sets are carefully **scaffolded** — early problems build intuition, later ones stretch.
- Use as inspiration for your **"Try It Yourself" challenges**.
- Adapt difficulty level to your learner (MIT undergrads are not typical learners).
- Many courses include **solutions** — useful for building your solution hints.

### 4. LECTURE VIDEOS
- Link as **"Further Learning" resources** for the learner.
- Videos may be from older semesters but concepts are timeless for fundamentals.
- Some lecturers are exceptional teachers:
  - **Gilbert Strang** (18.06 Linear Algebra) — legendary
  - **Erik Demaine** (6.006, 6.046J, 6.851 Algorithms) — engaging, creative
  - **Patrick Winston** (6.034 AI) — masterclass in lecturing
  - **Robert Morris & Frans Kaashoek** (6.824 Distributed Systems) — practical + deep

### 5. EXAMS
- Use for **diagnostic assessment** — can the learner solve an MIT exam problem?
- Good for designing your **Phase 3: APPLY** challenges.
- Many include solutions — helps you build scaffolded hints.

## OCW Relevance Check

OCW content is academic and some courses are from years ago. Always verify:

### CHECK 1: Is the concept stable?
```
ALWAYS RELEVANT (regardless of course year):
  - Algorithms (sorting, searching, graphs, DP)
  - Data structures (trees, hash tables, heaps)
  - Mathematics (linear algebra, calculus, probability)
  - Physics (mechanics, E&M, thermodynamics)
  - CS theory (automata, complexity, computability)
  - Systems concepts (caching, concurrency, networking protocols)

CHECK CAREFULLY (concepts stable but implementations evolve):
  - Programming language courses (syntax may be outdated)
  - Database courses (SQL is stable, but NoSQL/NewSQL are new)
  - OS courses (concepts timeless, specific APIs change)
  - Security courses (threats evolve rapidly)

USUALLY OUTDATED (prefer current sources):
  - Web development courses
  - Mobile development courses
  - Cloud/DevOps courses
  - Any course about specific tools or frameworks
```

### CHECK 2: Is the teaching approach still valid?
- MIT's pedagogical approach is almost always excellent
- But industry conventions may differ from academic style
- Bridge the gap: "MIT teaches it this way, in practice you'd..."
- Academic code examples may not follow current industry style guides

### CHECK 3: Is there a newer version of the course?
- Search for multiple semesters: `"site:ocw.mit.edu 6.006 2020"`
- Prefer the most recent offering
- Some courses have been **significantly updated**:
  - 6.006 (2020) is much better organized than older versions
  - 18.06 has multiple excellent versions (Strang's are all good)
  - 6.S191 (Deep Learning) is updated yearly

## Integration with Teaching Methodology

### For Interactive Teaching (Phase 2: BUILD)
- Use MIT's topic ordering as your concept sequence
- Adapt MIT's examples to be more conversational
- Simplify MIT's formalism for non-MIT-level learners
- Keep MIT's rigor on the concepts but make the language accessible

### For Course Building (--course mode)
- Use MIT's syllabus as a starting point for your outline
- MIT courses are typically 12-14 weeks × 3 lectures = 36-42 topics
- Compress: group 3-4 MIT lectures into 1 tutorial
- Maintain MIT's progressive complexity but add practical projects

### For Challenge Design
- MIT problem sets are gold for challenges, BUT:
- They assume MIT-level math background — simplify
- They often use pseudocode — convert to real language examples
- They grade on proofs — your challenges should be runnable code
- Take the **concept** of an MIT problem, reframe as a practical task