A Four‑Year Stanford AI Curriculum Guide Compiled by Mihail Eric

Author Mihail Eric, a Stanford computer‑science graduate who later worked as a data scientist at Amazon Alexa AI, has compiled a comprehensive four‑year curriculum for self‑studying artificial intelligence, based on the courses he took during his undergraduate and graduate studies.

Year 1 – Foundations: Focus on core computer‑science concepts and software fundamentals. Recommended courses include CS 106B (Programming Foundations), CS 107 (Computer Systems), CS 161 (Algorithm Design and Analysis), CS 109 (Probability), EE 103 (Linear Algebra) and Math 51 (Multivariable Calculus). Each entry lists the course code, a brief description of the topics covered, and a link to the official Stanford page.

Year 2 – System Knowledge: Deepen understanding of AI principles and system design. Suggested courses are CS 221 (Artificial Intelligence), CS 143 (Compilers), CS 145 (Databases), CS 149 (Parallel Computing) and CS 140 (Operating Systems), again with descriptions and URLs.

Year 3 – Advanced Topics: Concentrate on machine‑learning theory and specialized AI domains. Courses include CS 229 (Machine Learning), EE 364A (Convex Optimization), CS 228 (Probabilistic Graphical Models), CS 246 (Data Mining) and CS 224N (Natural Language Processing), each explained and linked.

Year 4 – Practice: Emphasize hands‑on projects, research, and industry experience. Advice covers joining campus projects (e.g., CS 341), participating in research groups, securing internships, building datasets, and iterating on models. The article stresses that practical implementation is essential for a successful AI career.

The guide concludes that completing these courses (or a tailored subset) equips learners with the knowledge and skills needed for a career in machine learning or data science, while encouraging continuous practice and exploration of emerging AI fields.