Year-round AI Courses

AlphaStar’s Year-round AI Program is composed of fall and spring terms. A year-round course is composed of 2 hours per week for 16 weeks per term, in total 32 class hours in one semester. In addition, weekly 1 hour is required for homework on average (48 hours in total per term). The year-round course (Fall + Spring) is equivalent to the summer course.

NOTE: In year-round courses, guided practice time is less than summer camps. Students need to continue to write codes for the solutions discussed in class as homework.

Overview

Dates and Registration

Curriculum

Levels and Courses

Diagnostic Exams

Faculty

Fees

  • Online Live: $925
  • In-person: $1,300

⇒ Online Live/In-person Super Early Registration
by Jun 15, 2026
Discounts: Online live – $100, In-person – $150

⇒ Online Live/In-person Early Registration
by Aug 1, 2026
Discounts: Online live – $50, In-person – $75

Available Discounts

Course Schedule

Register Now

Dates

Fall: Aug 19-23, 2026 – Dec 9-13, 2026
Spring: Jan 6-10, 2027 – Apr 21-25, 2027

Academic Curriculum

The students will be equipped with necessary background in lectures and trained with different types of problems to master various problem solving techniques. The classes are problem solving-based and the curriculum is aligned with USA AI Olympiad (USAAIO). Please check USAAIO Website for more information about USAAIO.

Levels and Courses

Below is a short description of each level. For more details and diagnostic exams, please

click for details.

Programming:

In machine learning, the main programming language is Python. This level has two Python courses: CS21F-1 and CS21F-2. These courses do not require programming background.

Data Science:

This level’s course is AI25F and it is for students who finished Python courses and have a Pre-algebra level math background. The course teaches the basics of data science: how to analyze data, find patterns, and communicate insights using Python. Students also complete real projects and learn how to use AI tools to work faster and smarter. By the end, they develop projects that can be geared towards local or regional STEM project competitions.

Machine Learning:

This level’s course is AI31F. Students learn how core machine learning algorithms work and how to apply them in Python to real-world datasets. By the end, they develop projects that can be geared towards top-tier STEM competitions such as ISEF or Broadcom MASTERS.

Deep Learning:

This level’s course is AI41F. This course introduces neural networks and modern deep learning workflows. Students learn the core building blocks of neural networks, develop intuition for how training works, and explore optimization techniques for stable learning. By the end, they develop projects that can be geared towards top-tier STEM competitions such as ISEF or Broadcom MASTERS.

AI Olympiad:

There are two courses in this level: AI51F-1 and AI51F-2. These courses focus on theoretical grounds of AI courses and math topics required for the USAAIO. USAAIO level problems will be solved.

Diagnostic Exams

If you have further questions about levels, please send us an email at info@alphastar.academy

Please click for diagnostic exam

Faculty

AlphaStar Academy mainly considers teaching, competition, and education background as well as passion and dedication for the subject when hiring full-time and part-time teachers.
We hire our instructors and TAs from a pool of High School, College, Ph.D. students, school teachers, and University Professors. Our faculty have teaching/coaching/tutoring experience and have expertise in the subject area, regardless of their age.
They have participation and/or training experience in national/international math competitions and Olympiads in Math, CS, and Physics such as USAMO, USACO, USAAIO, USAPhO, IMO, IOI, and IPhO.
They are role models and inspiration for students with their backgrounds and achievements.

AlphaStar Year-round AI Course Faculty and guest lecturers for upcoming online live/in-person camps and former camps are listed below with their most recent bios.

  • 2026-2027
    • /
  • Former
    • /

Fatih Gelgi, Ph.D..

  • AlphaStar Co-founder and CS / AI Dept. Director
  • Ph.D., Computer Science (in Machine Learning), Arizona State University (2007)
  • International Olympiad in Informatics (1999: Bronze Medal)
  • USA Computing Olympiad Coach (2006-2014)
  • Olympiad in Informatics Turkish National Team Coach (1999-2003)
  • Balkan Olympiad in Informatics (1998, 1999)
  • Computing Olympiad Coach (25+ years)

Yasin Ceran.

  • Assist. Prof., San José State University (2022-present)
    School of Information Systems and Technology
    Specializations: AI, Machine Learning, Data Analytics
  • Assoc. Prof., Korea Advanced Institute of Science and Technology (KAIST) (2021-2022)
  • Assist. Prof., Santa Clara University (2013-2020)

Alexander Moreira, M.S..

  • M.S. in Computer Science, Stanford (2023)
  • B.S. in Mathematics, Stanford (2022)
  • Mathematics and Computer Science instructor (5+ years)
  • George Polya Prize from Stanford University (2022)
  • Honorable mention for the Computing Research Association’s Undergraduate Researcher Award (2022)

Salma Baig, M.A., M.S..

  • AlphaStar Instructor (since 2018)
  • Computer Science teacher (15+ years)
  • MS in Computer Science, Georgia Tech (2021)
  • MA in Education, University of London (1998)

Asuman Celik, Ph.D..

  • Adjunct Professor, U of Cincinnati (2021-present)
  • Ph.D. Candidate in Information Technology (focused on AI applications in Cancer Research, expected in 2025)
  • Ph.D. in Biomedical Informatics (2022)
  • B.S. in Computer Science (2020)
  • ACM-ICPC, Co-Coach, Contestant, 2017-2018
  • Teaching Computer Science (including machine learning and deep learning since 2017)

Connect With Us

100 Saratoga Ave Suite 100
Santa Clara, CA 95051 United
States +1 (650) 273-4089

Our programs & courses are now Fully Accredited by the Accrediting Commission for Schools, Western Association of Schools and Colleges