AII 600. Foundations and Practice of Machine Learning for Artificial Intelligence. MS in AI

George Mason University
Fall 2025

Course Material

Textbook

Canvas HW Submission

Instructor: Vadim Sokolov

Location: Engineering Building, Room 2608
Time: Wednesdays 7:20 - 10:00 pm
Office hours: By appointment
Prerequisites: Graduate standing (Undergraduate engineering math: Calculus, probability theory, statistics, and some basic computer programming skills.).
HW Logistics: You will submit your HW and projects to Canvas

Content and goals

Introduces the foundations of machine learning encountered in AI with emphasis on practical aspects. Students learn to analyze complex datasets, identify problems benefiting from AI solutions, and implement solutions using appropriate libraries and computing platforms while evaluating them against AI risk frameworks.

List of Topics

  • Probability and Bayes
  • Decision Making
  • Modeling using known distributions
  • Frequentist and Bayesian Inference
  • AB Testing
  • Regression, Classification and
  • Mixture of expert models
  • Tree-based methods
  • Temporal data and forecasting
  • Deep Learning
  • Optimisation and Regularization
  • Model Selection (Bias-Variance, Double Descent)
  • AI Risk Frameworks

Schedule

  • Aug 27: First Class
  • Sep 10: HW 1 Due
  • Sep 17: HW 2 Due
  • Sep 24: HW 3 Due
  • Oct 1: HW 4 Due
  • Oct 8: In-class Midterm
  • Oct 22: HW 5 Due
  • Nov 5: HW 6 Due
  • Nov 5: Final project proposal due
  • December 3: Final Project Presentations (last class)

Case Studies

  1. Self-Driving Cars and the Uber Fatal Crash (2018) Focus: Safety, liability, trust in AI, limitations of perception models Key Questions:

    • What caused the failure in perception?
    • Who is responsible—the AI developer, the safety driver, or Uber?
    • Should fully autonomous driving be paused until X?

  2. COMPAS Algorithm in Criminal Justice Focus: Algorithmic bias, fairness, transparency Key Questions:

    • Why did COMPAS exhibit racial bias?
    • Can AI be truly unbiased?
    • Should such models be used in sentencing?

  3. AI in Healthcare: IBM Watson for Oncology Focus: Promise vs reality, hype cycle, trust in clinical AI Key Questions:

    • Why did Watson fail to meet expectations?
    • What lessons can we learn for future medical AI systems?

  4. Deepfakes and Synthetic Media Focus: Misinformation, regulation, detection vs creation arms race Key Questions:

    • Who should be held accountable for deepfake misuse?
    • Are technical detection solutions sufficient?

  5. ChatGPT and Large Language Models in Education Focus: Plagiarism, learning integrity, productivity vs dependence Key Questions:

    • Should universities ban or embrace AI tools in coursework?
    • How do we ensure critical thinking is preserved?

  6. AI in Hiring: Amazon’s Recruitment Tool Bias Focus: Gender bias, explainability, data-driven discrimination Key Questions:

    • Why did the hiring algorithm become biased?
    • How should companies audit AI systems for fairness?

  7. AI in Warfare: Lethal Autonomous Weapons (LAWS) Focus: Ethics, accountability, international law Key Questions:

    • Should AI be allowed to make life-and-death decisions?
    • What governance models could prevent misuse?

  8. AI for Social Good: Predictive Models for Disease Outbreaks (e.g., BlueDot and COVID-19) Focus: Success stories, limitations, data privacy Key Questions:

    • Why was BlueDot successful in early COVID detection?
    • How can similar models be scaled responsibly?

Format:

  • Quick Context (5 min)
  • Small Group Brainstorm (10 min)
  • Round Table Reporting and debate (15 min)
  • Instructor Wrap-Up + Reflection (15 min)

Assignments

Students will have a in-class midterm exam and final project. There are 6 homework assignments; students are encouraged to work in small groups. Each homework has 2-3 theoretical questions and 2-3 hands-on problems. Theoretical questions will be based on the material covered in class. Hands-on problems will require using Python or R and routines provided by instructor to perform data analysis tasks. For the final project a student or a group of students can choose their own data set and a hypothesis to verify. Each group will submit the final report.

Computing

You can choose which software you use. I recommend investing the time to learn Python and R.

A great way to start learning is do a data camp or coursera course.

Grading

Grade based entirely on participation in class, homework assignments, in-class midterm and final project.

Midterm 35% + Final project + 35% + Homework 30%. Scores of each component are normalized to be out of 100. Grades will be posted on (D. Cut-offs: 97 (A+), 93 (A), 90 (A-), 87 (B+), 82 (B), 79 (B-), 77 (C+), 73 (C), 70 (C-), 67 (D+), 60)

Optional Textbooks

  • Diez, Barr and Cetinkaya-Rundel Statistics, OpenIntro, 2015
  • James, Witten, Hastie and Tibshirani, R, Springer, 2009.
  • Kuhn and Johnson, Modeling, Springer, 2013.
  • Hyndman and Athanasopoulos, Practice, OTexts, 2013.

Mason Honor Code

To promote a stronger sense of mutual responsibility, respect, trust, and fairness among all members of the George Mason University community and with the desire for greater academic and personal achievement, we, the student members of the university community, have set forth this honor code: Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work. Students are responsible for their own work, and students and faculty must take on the responsibility of dealing with violations. The tenet must be a foundation of our university culture.

All work performed in this course will be subject to Mason’s Honor Code. Students are expected to do their own work in the course. For the group project, students are expected to collaborate with their assigned group members. In papers and project reports, students are expected to write in their own words,

Individuals with Disabilities

The university is committed to providing equal access to employment and educational opportunities for people with disabilities.

Mason recognizes that individuals with disabilities may need reasonable accommodations to have equally effective opportunities to participate in or benefit from the university educational programs, services, and activities, and have equal employment opportunities. The university will adhere to all applicable federal and state laws, regulations, and guidelines with respect to providing reasonable accommodations as necessary to afford equal employment opportunity and equal access to programs for qualified people with disabilities.

Applicants for admission and students requesting reasonable accommodations for a disability should call the Office of Disability Services at 703-993-2474. Employees and applicants for employment should call the Office of Equity and Diversity Services at 703-993-8730. Questions regarding reasonable accommodations and discrimination on the basis of disability should be directed to the Americans with Disabilities Act (ADA) coordinator in the Office of Equity and Diversity Services.