AII 600: Foundations and Practice of Machine Learning for Artificial Intelligence
MS in AI. Fall 2026. George Mason University
Textbook. Labs. Final project. Tutor setup. Submit work on Canvas.
| Instructor | Vadim Sokolov, vsokolov@gmu.edu |
| Office / hours | Nguyen Engineering Building 2242; by appointment |
| Meets | Wednesdays 7:20–10:00 pm, Enterprise Hall 276 |
| Credits | 3 |
| Recommended preparation | Calculus, probability, statistics, and some programming. If the calculus or matrices for OLS, MLE, and SGD are rusty, use the math background notebook. |
| Enrollment | College of Engineering and Computing. Class standing Advanced to Candidacy, Graduate, Junior Plus, Non-Degree, or Senior Plus. Non-degree undergraduates may not enroll. |
Schedule
No class 25 November (Thanksgiving). Week 15 is the exam block, 7:30 to 10:15 pm. Weeks 1 through 7 are lecture and labs. Week 8 was the midterm. Weeks 9 through 14 are lecture and labs alongside the project. Week 15 is the defense.
| Wk | Date | Topic | Work |
|---|---|---|---|
| 1 | 26 Aug | Probability; AI/ML landscape | Tutor; Lab 1 assigned |
| 2 | 2 Sep | Distributions, Bayes, correlation | Lab 1 due; Lab 2 assigned |
| 3 | 9 Sep | CDFs, quantiles, method of moments | Lab 2 due; Lab 3 assigned |
| 4 | 16 Sep | MLE, likelihood, CLT | Lab 3 due; Lab 4 assigned |
| 5 | 23 Sep | OLS, residuals | Lab 4 due; Lab 5 assigned |
| 6 | 30 Sep | Residual analysis, transforms | Lab 5 due; Lab 6 assigned |
| 7 | 7 Oct | Logistic regression, log loss; thresholds, expected cost | Lab 6 due |
| 8 | 14 Oct | Midterm | Lab 7; Midterm (2.5 hr); practice midterm, study guide, formulas in words, problem bank |
| 9 | 21 Oct | From the odds to a bet | Choose solo or a group |
| 10 | 28 Oct | Decisions: Kelly, utility, winner’s curse, the value of a test | Proposal; Lab 8 assigned |
| 11 | 4 Nov | Experiments, Bayes learning, bandits | Mid-build; Lab 8 due; Lab 9 assigned |
| 12 | 11 Nov | Trees; networks begin | Lab 9 due; Lab 10 assigned |
| 13 | 18 Nov | Model selection, regularization, priors | Lab 10 due; Lab 11 assigned |
| 14 | 2 Dec | Training: automatic differentiation, SGD, GPUs | Decision log; Lab 11 due; Lab 12 assigned, due 7 Dec |
| 15 | 9 Dec | Defenses | Defense |
University calendar: first day of classes 24 August; Labor Day 7 September; Fall Break 12 October; Thanksgiving recess 25–29 November; last day of classes 7 December; reading day 8 December; exam period 9–16 December.
Class Material
A deck can cover more than one week.
- Foundations
- Estimation and regression
- Classification and generalization
- Lab 2
- Lab 3
- Lab 4
- Lab 5
- Lab 6
- Lab 7: Linear regression and logistic regression
- Lab 8: Decisions
- Lab 9: Experiments, Bayes learning, and bandits
- Lab 10: Trees, then networks begin
- Lab 11: Model selection, regularization, priors
- Lab 12: Training at scale
Midterm
Practice midterm. Midterm study guide. Formulas in words. Problem bank. Math background.
It was week 8, the whole class, 2.5 hours, pen and paper. One double-sided cheat sheet, printed or handwritten. No calculator: every calculation was short enough to do by hand, and fractions were fine as answers. No AI assistant, and no laptop, phone, or watch. The exam was seven problems and 100 points: joint and conditional distributions, Bayes rule, the binomial, a normal model for a supplier decision, linear regression, logistic regression, and true-or-false concepts. That score is 40% of the course. The practice midterm, the study guide, the formulas in words, and the problem bank match that exam.
What this course is
The foundations of statistics and machine learning: probability, estimation, regression, classification, decision-making under uncertainty, model selection, and deep learning.
The term is in two parts. Weeks 1 through 7 are lectures and self-check labs. Week 8 was the in-class midterm, with no AI assistant. From week 9 the lectures and labs continue alongside the project. You may do it alone, or in a group of up to three. You choose the group, and you tell me by the start of week 9. The project is a real decision, on data whose provenance you have checked, with a stated loss. You choose and validate a model, compare an alternative, and defend the recommendation live in week 15. There is no report and no separate presentation.
The foundation phase is 60%: labs 20 and the midterm 40. The project phase is 40%, the defense. The labs are the 20% Preparation. There are twelve labs, Labs 1 to 7 before the midterm and Labs 8 to 12 after it, and each is one twelfth of the 20%. Each lab is due before the next class; Lab 12 is due 7 December, the last day of classes. Of the defense, 25 points are Understanding and 15 are Judgment.
Understanding means explaining mechanisms, interpreting results in context, applying the foundations without an assistant, predicting what changes when an assumption or condition changes, and explaining the analysis you produced. A correct number by itself is not enough. Judgment means stating the purpose, comparing reasonable alternatives, accounting for the consequences of error, calibrating claims to evidence, and identifying what would change the choice. Preparation is the labs: submit them on time and the 20% is yours. I do not mark them. The midterm was how I checked that the work became yours.
Given that AI assistants can write the code, fit the models, and make the plots, I will not grade a take-home write-up, script, or report as a document.
Using AI in this course
You are expected to use AI assistants. Getting results from one is fine. Self-check labs are Preparation: submit them on time for the 20%. I do not mark the lab. What is left that I can still attach to you is the project defense.
Some rules:
- Use whatever tools you want. There is no approved list. The midterm is the exception: no AI assistant and no computer. Pen and paper and one double-sided cheat sheet (printed or handwritten). No calculator: every calculation is short enough to do by hand. No laptop, phone, or watch. That exam is how I check that you can apply the basic principles and concepts on your own. Tool choice does not override data-use agreements, privacy obligations, or law. Do not upload confidential, restricted, regulated, or identifiable data to an external assistant unless the data owner has authorized that use.
- Disclose what you used and for what. No penalty attaches to the disclosure. Each decision-log entry: one line. If you used an assistant for everything and can defend all of it, that gets full marks.
- You are responsible for everything you submit, including the parts you did not write. If you cannot explain it, that is on you. You will encounter AI-produced work you do not initially understand. Interrogate it before submitting and defending it. I would rather that happen here than at work.
Course tutor
The course tutor is an assistant configured for this course. It answers R and Python questions directly, asks you a question back before explaining a concept, and will not answer “which model is better?” until the purpose of the analysis is clear. Set it up once in week 1, about ten minutes.
You can point the assistant at the URLs, without downloading anything:
Or download those two files from this page and load them in whichever assistant you use.
- Cursor, Claude Code, or a similar coding tool:
@the two URLs, or put both files in your course folder and settutor-instructions.mdas the tool’s instructions file. Keepcourse-context.mdin that folder so the tool can read it. That is the version of this with the least friction. I suggest Cursor; a $20 month ($16 if you pay for a year) is enough for the course. - Claude or ChatGPT, if your account has Projects: create a Project, paste
tutor-instructions.mdinto the project instructions, and uploadcourse-context.mdas a project file. Use that project for the term. - Assistants without Projects: start a new conversation, paste both files into the first message, and add the line “Use these as your instructions for this conversation.” Use one conversation per week and paste the files again; long threads get worse.
These files are platform-neutral, although assistants vary in how reliably they follow them. If you prefer a different one, paste the same two files into it. You can edit the instructions; it is a text file.
What you will be able to do by December
By the midterm.
- Apply and explain the basic principles of probability, estimation, regression, classification, testing, predictive values, and expected value to a new problem without an assistant, interpret the numbers in context, and say what changes if an assumption or condition changes.
- Diagnose a defective analysis from its output: name the defect, point at the evidence, and predict which way the corrected number moves.
By the end of the project.
- Choose, fit, and validate an appropriate model in R or Python, using evidence not used to fit it. Compare it with a simpler or more complex alternative, and say why that choice, including the representation, serves the stated purpose.
- State what each kind of error costs, and set evaluation measures, thresholds, and loss functions accordingly rather than by default.
- Quantify uncertainty and say what it means for the decision at hand, rather than reporting an interval and moving on.
- Establish where data came from and whether it is what it claims to be, then judge whether it supports the proposed use, who is affected, how errors fall, and who is missing from it.
- Delegate analytical work to an AI assistant and stay accountable for it: decide what to hand over, verify what comes back, and explain work you did not write.
- Defend an analytical choice under live questioning, and change your mind out loud when the questioning is right.
Grading
| Phase | Component | Weight | Understanding | Judgment | Preparation |
|---|---|---|---|---|---|
| Foundation | Self-check labs | 20 | 0 | 0 | 20 |
| Foundation | Midterm | 40 | 40 | 0 | 0 |
| Project | Defense | 40 | 25 | 15 | 0 |
| Total | 100 | 65 | 15 | 20 |
Scores of each component are normalized to be out of 100. Grades will be posted on Canvas. This course uses Mason’s graduate regular scale: A+, A, A-, B+, B, B-, C, F. Cut-offs: 97 (A+), 93 (A), 90 (A-), 87 (B+), 82 (B), 79 (B-), 70 (C). Below 70 is F.
Computing
You may use R or Python. Install one in week 1. The notes are mostly R; Module 6 includes some Python. Use the language you will defend. Also set up git (a course folder with notes and finalproject tracked), markdown, Jupyter, and the course tutor. Lab 1 is that setup plus the first probability; submit a Jupyter notebook on Canvas. From Lab 2 the assignment is the notebook. If OLS, MLE, or gradient descent will be new as calculus or matrices, work math-background.ipynb before week 4. Start noticing datasets you have access to through work, research, or an organisation you are part of. You may do the project alone or in a group of up to three that you choose. Tell me by the start of week 9. The proposal is due week 10. Fork the final project repository.
University policies
Mason requires every course to make available the Common Course Policies (August 2026). They cover academic standards, accommodations for students with disabilities, FERPA and the use of Mason email, and Title IX. That addendum is part of this syllabus. The AI rules in this document are course-specific and sit on top of the university academic standards; they do not replace them. I am a required reporter under the Title IX policy in that addendum. Confidential options are listed there.