Super Syllabus
20+ topics. 300+ pages. Your job: Discover what calls to you.
Not: Read all of it.
Is: Explore with Claude to find your curiosity.
Banking model approach: Pre-digest complexity → give you bite-sized chunks → tell you what’s important
Problem-posing approach: Give you the full landscape → teach you to navigate complexity → trust you to discover what matters
The skill: Not “read summaries.”
The skill: “I have complexity. I have Claude. How do I make sense of this?”
This is the actual transferable skill.
In your career, you’ll encounter:
- 500-page technical specs
- Competing research papers
- Dense legal documents
- Complex codebases
Learning to navigate complexity with a cognitive partner is the course in microcosm.
- Upload all topic files to a Claude Project
- Spend 60-90 minutes exploring with Claude
- Save your conversation
- Submit:
- Your top 7 ranked topics
- 2-3 sentences explaining WHY each interests you
- Your Claude conversation link
- One thing you learned about HOW to use Claude for exploration
Deadline: Sunday, Feb 8, 11:59 PM
Before you ask Claude about topics, first ask:
“What am I trying to learn right now?”
Then pick your mode:
Goal: Get the lay of the land
Prompt:
“Claude, I’m looking at the CIS 5020 super-syllabus. Give me a 2-3 sentence summary of each topic, focusing on what makes each one unique.”
When to use: Starting from zero knowledge
Goal: See how topics relate
Prompt:
“I’m interested in [topic A]. What other topics in this collection connect to it? How do they relate?”
When to use: You found something interesting, want to explore adjacent topics
Goal: Understand real-world relevance
Prompt:
“For each topic, give me one concrete real-world example of where this algorithmic lens matters.”
When to use: Topics feel too abstract, want to ground them in reality
Goal: Imagine what you could build
Prompt:
“I’m thinking about doing a portfolio project on [topic]. What are 3-5 possible project directions I could explore?”
When to use: Thinking ahead to portfolio work
Goal: Discover interests you didn’t know you had
Prompt:
“I don’t know what interests me yet. Help me explore by asking me questions about what kinds of problems I find compelling.”
When to use: Genuinely unsure what calls to you
Goal: Really understand one topic
Prompt:
“I want to understand [topic] more deeply. Walk me through the ‘Why This Matters’ section and help me see why someone would care about this.”
When to use: One topic caught your attention, want to go deeper
Goal: Distinguish between similar topics
Prompt:
“What’s the difference between [topic A] and [topic B]? When would I use one lens vs. the other?”
When to use: Two topics seem similar, need to understand the distinction
That’s normal. That’s banking model damage.
You’ve been trained to:
- Avoid complexity
- Wait for someone to tell you what’s important
- Scroll past anything longer than a tweet
The solution isn’t to make it smaller.
The solution is to learn how to navigate complexity with a cognitive partner.
This is healing work.
All 21 algorithmic topics are available below. Click any topic to read the full document.
- Algorithmic Topics
- Advanced Data Structures
- Computational Geometry
- Distributed Algorithms: Consensus and Coordination
- Divide and Conquer Algorithms
- Dynamic Programming
- Graph Connectivity (Cuts, Biconnected Components)
- Graph Traversal (BFS, DFS, Topological Sort)
- Greedy Algorithms
- Linear Programming & Optimization
- Lower Bounds & Adversary Arguments
- Minimum Spanning Trees
- Network Flow & Matchings
- NP-Completeness & Reductions
- Online Algorithms
- Parallel & Distributed Algorithms
- Parameterized Complexity
- Randomized Algorithms
- Shortest Paths (Dijkstra, Bellman-Ford, Floyd-Warshall)
- Streaming Algorithms
- String Algorithms
- Strongly Connected Components
What’s in the ZIP:
- All 20+ topic markdown files
- This exploration guide (PDF)
- Example Claude prompts
Beyond algorithmic topics, you’re learning:
- How to use Claude to navigate unfamiliar domains (transferable skill)
- How to recognize genuine vs. surface interest (self-knowledge)
- How to synthesize across multiple sources (information literacy)
- How to ask good exploratory questions (metacognition)
- How to build understanding through dialogue (epistemic AI use)
This is teaching you to think WITH AI, not outsource thinking TO AI.
“60-90 minutes seems like a lot”
- It’s an investment in shaping a course around YOUR interests
- Most students find it engaging once they start
“What if I still don’t know after exploring?”
- That’s valuable data. Submit your conversation anyway. We’ll talk.
“Can I explore with a partner?”
- Yes, but each person submits their own rankings and reflection
“What if my interests change later?”
- That’s expected. We’ll have checkpoints throughout the semester.
