Lessons Learned from CS221 Fall 2025

December 13, 2025

The AI hype

I reached a breaking point. Anyone and everyone was talking about AI and I was feeling a tad bit left out. Friends and family at home were talking about investing in AI stocks, colleagues at work were talking about how AI was coming for their jobs, or how they were staying ahead of the curve. Even my boss was pressing me to develop in-house AI solutions for every corner of our organization. Every online discussion (from space exploration to making a burger) was being viewed through an AI lens. While frustrating, it shouldn't have been surprising given that almost the entire world had jumped on the ChatGPT bandwagon when it launched nearly three years ago. I didn't buy into the AGI/ASI hype, but I did recognize the value of incorporating AI tools into my workflow. From the early days, I experimented with Bing Chat, Cursor, M365 Copilot, ChatGPT, GitHub Copilot, and more to streamline tasks from drafting emails to prototyping code.

Yet through it all, one question gnawed at the back of my mind: I didn't truly understand how these AI models worked. As someone with a CS background, I had a surface-level understanding, but I wanted to go deeper, to truly grasp the fundamentals. The challenge was filtering the signal from the noise in what continues to be a chaotic AI landscape. Most people I spoke with offered hand-wavy explanations about vectors and tokens, while the technical content I found online felt impenetrable. Despite a solid foundation in math and statistics, I struggled to connect the dots. I read the seminal paper Attention is All You Need and explored dozens of repositories on OpenAI's GitHub. I could grasp most of it, but my understanding felt fragile, like a house of cards. I lacked a coherent mental model for organizing these concepts. Neural networks, backpropagation, softmax, gradient descent: they were all tangled threads in my mind. I needed to weave these threads together to build a deeper understanding and become a more knowledgeable member of the CS community.

One small step

Initially, I believed the only path to a solid understanding of AI was pursuing a PhD in Computer Science. I researched CS PhD programs and quickly realized it would require quitting my job, relocating to a new city, and living on a graduate student's budget. Having completed my Bachelor's in CS back in 2012, I was hesitant to upend my life for a doctorate. Perhaps a Master's degree would be less disruptive? As it turned out, that path would demand a similar level of commitment, just compressed into 2-3 years instead of 3-4.

There had to be a more practical way to ease back into academia. Years earlier, I had used MIT OpenCourseWare to revisit concepts from the Advanced Data Structures course, particularly B-trees. I wondered if they offered AI courses as well. They did: Techniques In Artificial Intelligence (SMA 5504). I attempted to dive straight into the lecture notes and assignments, but quickly became overwhelmed. The problem wasn't comprehension; rather, I found myself spiraling into tangential topics without the structure and guidance of a formal course.

One weekend, while struggling through Homework 1a of SMA 5504, I turned to YouTube for help understanding CSP (Constraint Satisfaction Problem) concepts. The algorithm suggested a Stanford CS221 lecture on Constraint Satisfaction Problems. After watching it, I was hooked. The clarity and depth with which Percy Liang and Dorsa Sadigh explained the material was exceptional. I immediately watched several more videos from the CS221 playlist, each one reinforcing my interest. This led me to discover the Artificial Intelligence Graduate Certificate program on Stanford's website. Browsing through the CS221 Fall 2024 course materials, I gained a clear picture of the curriculum and learning outcomes. I was impressed by Stanford CS's commitment to transparency and their willingness to make course information freely available. As I reviewed the class schedule, I realized that living on the East Coast was actually advantageous: I could work during the day, attend online lectures in the late afternoon, and join office hours in the evening if needed.

That July weekend, I made my decision. I set up my Stanford Online account and began the application for the Artificial Intelligence Graduate Certificate. The process was straightforward: proof of residency and citizenship, unofficial transcripts from my decade-old Purdue University degree, and a brief statement of purpose (roughly 250 words) explaining my motivation and relevant background. In my statement, I emphasized my interest in real-world AI applications, particularly in robotics and autonomous vehicles that must plan and make decisions under uncertainty. I addressed potential concerns about being a decade removed from academia by highlighting my graduate-level Statistics coursework, which provided a strong mathematical foundation. I also noted how my decade-long IT career had given me extensive practical experience with multiple programming languages and software development practices. With everything compiled and uploaded, I clicked submit and began the waiting game.

Drinking from a firehose

During the wait, I recruited an HR coordinator from work as my exam monitor for the course. I gave them an estimate of when the exams would occur based on historical CS221 websites (some quarters had a midterm and final), so they could plan accordingly. Exams tended to fall around holidays, and giving monitors advance notice helps everything run smoothly.

After nearly two months of waiting, about two weeks before class was set to start, I received my acceptance email for enrollment into the program and CS221. Since this was my first foray into Stanford's AI program, I had to complete some administrative tasks: setting up a SUNet account and updating my profile. In the days leading up to the first lecture, I bought online versions of the textbooks, kept tabs on Stanford's CS221 GitHub to see when the Fall 2025 site would go live, and finally got acquainted with the seemingly endless list of sites and apps we'd be using (I bookmarked every single one):

  • CS221: Artificial Intelligence: Principles and Techniques: The main hub where all course materials were published
  • Canvas: Stanford's general hub application meant to bring everything together. Works in theory, but in practice, direct bookmarks were easier
  • Ed: Forum for discussions where students could post questions about homework, exams, and projects
  • Panopto: Where recorded and live lectures were posted
  • Gradescope: All written and code submissions went here, including homework and projects

Beyond the course platforms, I also used:

  • Windows Subsystem for Linux (WSL): Made it easier to set up Python environments for coding on Windows
  • VS Code: For writing code for the programming portions of homework assignments
  • Overleaf: For typesetting the written portions of homework assignments

Day one hit, and with it came the first homework deadline: one week. The clock was ticking. The assignment looked innocuous enough: only about four problems. But don't be fooled. Unless you're an expert in math or coding, every single problem was a challenge. With work consuming my mornings and only three hours in the evenings for homework, I was on a serious time crunch. Every moment counted. This pattern repeated itself seven more times over the next ten weeks, roughly one homework per week, each with 4-5 problems. The only breathers came during the two weeks before the final exam and Thanksgiving week. Otherwise, it was a relentless onslaught: 20 hours per week split between two 90-minute lectures (Monday and Wednesday evenings) and the rest devoted to homework, readings, and participating in Ed discussions.

The class was so fast-paced that by the time you felt comfortable with one topic, you were thrown into the deep end of another one. Here's the barrage of topics that were covered:

Week 1:

  • Einstein Summation and einops
  • Tensor operations, broadcasting
  • Linear Algebra
  • Gradients
  • Optimization
  • Backpropagation
  • NeurIPS Ethical Guidelines

Week 2:

  • Linear classification
  • Bag-of-Words features
  • Logits, Softmax probabilities
  • Cross Entropy Loss
  • Multilayer Perceptron, deep learning
  • ReLU activation function
  • Gradient Descent, SGD

Week 3:

  • Dynamic Programming
  • Beam Search
  • Best of N Search
  • Uniform Cost Search
  • A* and heuristics

Week 4:

  • Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Value iteration
  • Policy iteration
  • Model-free Monte Carlo
  • SARSA
  • Q-learning

Week 5:

  • Policy gradient
  • Function approximation
  • Minimax algorithm
  • Alpha-beta pruning
  • Expectimax
  • Evaluation functions

Week 6:

  • Temporal difference (TD) learning
  • Nash equillibria
  • Non-zero sum games
  • Bayesian networks
  • Probabilistic inference
  • Marginalization

Week 7:

  • Rejection sampling
  • Gibbs sampling
  • Conditional independence
  • Hidden Markov models
  • Maximum Likelihood
  • Expectation maximization (EM) algorithm

Week 8:

  • Propositional logic
  • Knowledge bases
  • Entailment, contradiction and contingency
  • Inference rules
  • Soundness and completeness
  • First order logic
  • Semantics and formulas
  • Substitution and unification
  • Universal and existential qunatification

Week 9: (Week of final exam)

  • Large Language Models (LLMs)
  • Dual use technology
  • Alignment and copyrights
  • Openness and transparency

Week 10:

  • Economics of AI
  • Future of work
  • AI Supply chain

My two cents

Looking back on this journey, several key things that I learned:

1. Time management and organization is everything
Balancing a full-time job with a graduate-level course required ruthless prioritization and smart tooling decisions. Beyond maximizing every spare moment; reviewing lecture notes during lunch breaks, debugging code late into the evening, I invested in workflow automation early. I created a private GitHub repository with a shared virtual environment and preconfigured build commands for the grader, allowing all homework subfolders to reference common setup. This eliminated the hour-long setup ritual for each new assignment.

Similarly, while Overleaf worked for the first assignment, switching between VS Code and Overleaf became tedious. I configured LaTeX editing directly in VS Code for the remaining assignments, consolidating my entire workflow into a single environment. These upfront investments in tooling paid dividends throughout the quarter.

2. Ask questions
The Ed forum became an invaluable resource. Initially, I was hesitant to post questions, thinking it might become a distraction. But I quickly realized that many students had similar concerns, and the teaching staff was incredibly responsive and helpful. Going through all the questions and their answers, and trying to answer a few questions myself, helped improve my own understanding of the topics.

Attending office hours and exam review sessions proved equally valuable. Even when I felt confident about the material, these sessions revealed subtle nuances I'd missed and corrected misconceptions I didn't know I had. This deeper understanding translated directly into stronger intuition when approaching both homework problems and the final exam.

3. Foundations matter
Linear algebra, probability, and calculus turned out to require much deeper comprehension than I anticipated. Investing time early to solidify these fundamentals paid dividends throughout the course. For linear algebra, understanding tensors, dimensionality, and broadcasting proved essential. I constantly checked output dimensions to validate I was on the right track. For probability, mastering conditional probabilities and Bayes' Theorem was invaluable; and finally for calculus, a solid grasp of derivatives, partial derivatives, and gradients was crucial.

4. Pursue every extra credit opportunity
With 40% of the grade spread across 8 homework assignments and 60% tied to a single final exam, I approached the quarter strategically. From day one, I pursued every available extra credit point: answering questions on Ed, completing bonus problems on homework assignments, and starting the extra credit project (worth up to 1.5% of the final grade). Uncertain how I'd perform on the final, I wanted to build as much of a buffer as possible to protect against a below-average exam score. This strategy paid off! Once the final exam scores were released, I calculated that I'd secured an A and could safely abandon the extra credit project.

Final Thoughts

Was CS221 worth the grueling 20 hours per week on top of a full-time job? Absolutely. The course delivered exactly what I was looking for: a structured, comprehensive foundation in AI that connected all those tangled threads in my mind. I now understand not just how to use AI tools, but how they actually work under the hood. More importantly, I proved to myself that returning to academia after a decade wasn't impossible. It was challenging, demanding, and at times overwhelming; but also incredibly rewarding. The certificate program offers a practical middle ground between self-study and a full graduate degree, perfect for working professionals who want to deepen their expertise without upending their lives.

If you're considering taking CS221 or pursuing Stanford's AI Graduate Certificate, my advice is simple: go for it. Just make sure you're ready to commit the time and mental energy it requires. Clear your schedule, rally your support system, and prepare for an intense but transformative 3 months.

The AI revolution isn't slowing down, and now I feel equipped not just to keep up, but to contribute meaningfully to the conversation. Those hand-wavy explanations that used to frustrate me? Now I can provide the rigorous technical details behind them; and that feeling of finally understanding makes every exhausting evening worth it.