AI Office Hours

Each session, we take what actually happened in AI this week and make sense of it together — separating real shifts from press-release noise.
Not a one-way walkthrough. Here's how it runs:

• Story of the week — I bring 2–3 of the biggest developments and we unpack what they really mean, past the hype.
• Hot or not — live poll. I put a headline on screen, you vote: important or overblown? Then we argue.
• Bring your own headline — saw something that confused or excited you? Drop it in chat and we'll dig in as a group.
• Open floor — questions, predictions, disagreements all welcome.

Beginners leave understanding why the week's news matters and how it fits together. Folks already in the field get a sharp, opinionated read and a room to test their takes. Either way, you'll be able to hold your own in any "did you see what just dropped?" conversation.
No prerequisites. Bring a question — or a headline. Online via Zoom.

One ask: share our group on LinkedIn and other social media, and with friends — the more people, the better the conversations. 🙌

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

After our last event, the feedback was clear: people want to learn more about LLMs — and how to bring them into their own workflows. But before we can use them well, we need a real feel for how they work under the hood.
So this session goes back to the roots. A machine doesn't see words — it sees numbers. We'll follow that whole journey in plain English:

• Encoding — how raw language gets turned into numbers a model can work with (tokens and embeddings), and why that first step shapes everything after it.
• Representation — how models learn to understand a sequence: from early recurrent networks, LSTMs, and GRUs that tried to "remember" context, to where they hit their limits.
• Generation — the breakthrough that changed everything: attention and the Transformer, and how that one idea opened the door to the GPTs and LLMs we all rely on today.

The goal is intuition first — enough under-the-hood detail to make the "oh, that's why it works" moments click, with no deep learning background required. Just curiosity.

Once the foundation is down, future sessions get to the fun part: actually putting these models to work.

Got a question about AI you'd like us to tackle? Drop it in the comments — we'd love to fold it into the discussion.

Ever wonder why probability shows up everywhere in machine learning?

It's not just background theory — it's what actually shapes the loss functions you train on every day. In this hour, we'll trace where those losses come from.
Why do we reach for mean squared error on a regression task, but cross-entropy on a classification task? Most of us learned these as rules to memorize. They're not. Both fall straight out of probability theory — once you ask "what distribution generated this data?", the right loss basically derives itself.

We'll walk through:

• How MSE emerges from a Gaussian likelihood
• 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 the losses you already use in a completely different light.

One hour. Bring a notebook.

This is a highly interactive bootcamp — your questions are answered within 24 hours, and the whole program is built to prepare you thoroughly for the exam and give you the best shot at the certification.

A live, 4-weekend 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, and responsible AI.

Eight 90-minute working sessions, held Saturday and Sunday evenings across four weekends. Each session is interactive: 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.

Schedule — all sessions 7:00 PM ET, 90 minutes, live on Zoom
Weekend 1 — AI, ML & deep learning fundamentals

• Session 1 · Sat, Feb 6 — AI & ML foundations: core terminology, types of ML, the ML lifecycle
• Session 2 · Sun, Feb 7 — Deep learning essentials: neural networks and where DL fits

Weekend 2 — Generative AI, foundation models & prompt engineering

• Session 3 · Sat, Feb 13 — Generative AI & foundation models: what they are and how they work
• Session 4 · Sun, Feb 14 — Prompt engineering: techniques, patterns, and common pitfalls

Weekend 3 — The AWS AI/ML stack

• Session 5 · Sat, Feb 20 — Amazon Bedrock and the managed AI services
• Session 6 · Sun, Feb 21 — Amazon SageMaker and the ML workflow on AWS

Weekend 4 — Responsible AI & exam readiness

• Session 7 · Sat, Feb 27 — Responsible AI, security, and governance
• Session 8 · Sun, Feb 28 — Exam strategy: question framing, pacing, and full review

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

• 8 live sessions · ~12 hours total instruction
• $350 per person for the full bootcamp
• Small group; questions welcome throughout, we will have a teaching assistant manage the Class;

Bring your questions and something to take notes with.