Program Overview

King Abdullah University of Science and Technology

The King Abdullah University of Science and Technology offers a range of programs and courses.

Programs of Study

The university provides various programs, including:

• Applied Mathematical and Computational Science (AMCS)
• Applied Physics (AP)
• Bioscience (B)
• Biological and Environmental Science and Engineering (BESE)
• Bioengineering (BioE)
• Chemical Engineering (CE)
• Computer, Electrical and Mathematical Sciences and Engineering
• Chemistry (Chem)
• Computer Science (CS)
• Electrical and Computer Engineering (ECE)
• English
• Environmental Science and Engineering (EnSE)
• Energy Resources and Petroleum Engineering (ERPE)
• Earth Science and Engineering (ErSE)
• Marine Science (MarS)
• Mechanical Engineering (ME)
• Material Science and Engineering (MSE)
• Physical Science and Engineering (PSE)
• Plant Science (PS)
• Statistics (STAT)
• Technology Innovation and Entrepreneurship (TIE)
• Winter Enrichment Program (WE)

Courses

The university offers a variety of courses, including:

• AMCS - Applied Mathematical and Computational Science
  • 100
  • 200
    • AMCS 200
    • AMCS 201
    • AMCS 202
    • AMCS 203
    • AMCS 204
    • AMCS 206
    • AMCS 211
    • AMCS 212
    • AMCS 214
    • AMCS 215
    • AMCS 229
    • AMCS 231
    • AMCS 232
    • AMCS 235
    • AMCS 237
    • AMCS 241
    • AMCS 249
    • AMCS 251
    • AMCS 252
    • AMCS 253
    • AMCS 255
    • AMCS 271
    • AMCS 272
    • AMCS 293
    • AMCS 294
    • AMCS 295
    • AMCS 297
    • AMCS 299
  • 300

AMCS 215 Mathematical Foundations of Machine Learning

Course Description

The course introduces mathematical foundations underlying modern algorithms for regression, classification, clustering, and dimension reduction in data-rich settings. These mathematical tools, needed to understand machine learning algorithms, are traditionally taught in disparate courses, making it hard for ML students to efficiently learn them. The course bridges a gap between mathematical and machine learning courses, introducing the mathematical concepts with a minimum of prerequisites and in the context of machine learning and data science applications. The goal is to build intuition into these mathematical concepts and practical experience with applying them. Numerical computations and applications with real data will accompany the theory. This course is meant to complement application-oriented machine learning and data science courses.

Credits

3

Prerequisite

Basic familiarity with undergraduate-level concepts from calculus, linear algebra, and statistics. Ability to write simple programs and scripts.