AI Office Hours

Everyone's talking about large language models, but here's the question nobody starts with: how does a machine even represent a word in the first place? A computer only understands numbers — so before any of the ChatGPT magic can happen, "king," "queen," and "dog" have to become vectors of numbers. And how you do that turns out to be the whole ballgame.

This session goes back to the idea that cracked it open: Word2Vec. It's the technique that first showed you could capture a word's meaning as a position in space — close words end up close together, and you get the famous result that king − man + woman ≈ queen. Meaning becomes geometry. Once you see this, modern LLMs stop feeling like magic and start feeling like a natural next step.
We'll walk through, intuitively:

• Why words become vectors — the core idea of embeddings, and why "similar meaning = nearby in space" is so powerful
• Continuous Bag of Words (CBOW) — predicting a word from the words around it
• Skip-gram — flipping it around: predicting the surrounding words from a single word, and why it shines on rarer words
• Why any of this matters for today's LLMs — how these early ideas grew into the token embeddings inside every model you use now

No heavy math required — this is built to be intuitive and beginner-friendly. Come with curiosity and your questions. We'll wrap with an open Q&A (~30 min) so you can ask whatever's been nagging you about how these models actually work.
Great for beginners. Bring your questions.

Ever wonder why we train regression models on mean squared error?
Most of us learned MSE as a rule to memorize: regression task → square the errors → minimize. But it's not an arbitrary choice. MSE falls straight out of probability theory — once you ask "what distribution generated this data?", the loss basically derives itself.
In this focused hour, we'll trace exactly where MSE comes from:

• How MSE emerges from a Gaussian likelihood
• What "minimizing squared error" is really doing under the hood
• Why this reframing makes the loss landscape feel principled instead of arbitrary

Come with basic ML familiarity and your questions — this is meant to be interactive, not a lecture. You'll leave seeing a loss function you already use every day in a completely different light.
One focused hour. Bring a notebook.

The first AI Office Hours runs like a class — relaxed but structured.
I'll walk through where AI actually is in 2026: what LLMs, image models, and agents really do, what they don't, and how to read past the hype. Beginners will leave with a real mental model; folks already working with these tools should pick up some sharper angles. We'll close with open Q&A.
No prerequisites. Bring a question. Online via Zoom.

Hosted by Bob (Jue) Guo — CS PhD candidate and adjunct faculty at the University at Buffalo, teaching deep learning, machine learning, and AI.

AWS Certified AI Practitioner Bootcamp
A live, 4-week bootcamp built to prepare you for the AWS Certified AI Practitioner exam (AIF-C01) — AWS's foundational credential covering AI/ML fundamentals, generative AI, foundation models, responsible AI, and security.
This is a highly interactive bootcamp — not a lecture series. We work through the exam's domains, unpack the concepts people most often trip on, and look at how the questions are actually framed so your prep stays targeted. Questions are answered within 24 hours.
Format

• 12 live sessions over 4 weeks — Monday, Wednesday & Friday
• 5:00–6:30 PM ET (1.5 hours each), live on Zoom
• ~18 hours of total instruction
• Small group; questions welcome throughout

Schedule
Week 1 — AI & ML foundations

• Mon, Aug 3 — Core terminology & types of machine learning
• Wed, Aug 5 — The ML lifecycle and where deep learning fits
• Fri, Aug 7 — Tour of the AWS AI/ML landscape

Week 2 — Generative AI & foundation models

• Mon, Aug 10 — Foundation models: what they are and how they work
• Wed, Aug 12 — Prompt engineering: techniques and pitfalls
• Fri, Aug 14 — Applications: RAG, fine-tuning, and when to use each

Week 3 — The AWS AI stack in practice

• Mon, Aug 17 — Amazon Bedrock and the managed AI services
• Wed, Aug 19 — Amazon SageMaker and the ML workflow
• Fri, Aug 21 — AI services in practice (Comprehend, Textract, Rekognition)

Week 4 — Responsible AI, security & exam readiness

• Mon, Aug 24 — Responsible AI: fairness, bias, and transparency
• Wed, Aug 26 — Security, compliance, and governance on AWS
• Fri, Aug 28 — Full review, exam strategy & practice questions

Who it's for
Anyone preparing for the AIF-C01 exam. No prior AWS or coding experience required — suitable for developers, analysts, PMs, and career-switchers building AI literacy.
Details

• 12 live sessions · ~18 hours total
• $350 per person for the full bootcamp
• Zoom link sent to registrants before each session

Please note: This bootcamp is designed to give you a strong, structured path to passing — but a passing result isn't guaranteed. The exam is administered independently by AWS, and your outcome depends on your own preparation alongside the sessions.
Bring your questions and something to take notes with.