Artificial Intelligence System Safety (AISYS)

Course Description

Artificial Intelligence and autonomous systems have become pervasive in software applications. However, the implications for AI safety has created new concerns and risk factors especially for autonomous vehicles. This course provides a thorough survey of AI, machine learning, optimization, and autonomous vehicle techniques followed by safety and hazard analysis methods. Along the way, we will bring clarity to definitions, actual capabilities of AI systems, and the current state of data science. Case studies and real-world incidents will also provide learnings and insights to further advance the goal of AI safety.

Objectives: To provide a survey of current AI and autonomous vehicle techniques as well as discovering and mitigating their hazards.   This course will also apply existing software safety methods to artificial intelligence and establish why AI is just another tool in a suite of software functionalities.

Who Should Attend: Engineers, Program Managers and Senior Managers having responsibility for the development and acceptance of systems operating with AI/ML.

Prerequisites: Individuals should be familiar with the concepts and processes of System Safety and Software Safety.

AISYS 25-1
AISYS 25-2
AISYS 26-1
16-20 Sep 2024
17-21 Mar 2025
15-19 Sep 2025

Course Outline

  1. Definitions and Concepts
  2. AI and ML Capabilities
    • Optimization and routing
    • Metaheuristics and tree search
    • Machine Learning
    • Deep Learning
    • Simulations
    • Reinforcement learning
  3. ML Explainability
    • Data analysis and preparation
    • Statistical methods
    • Scientific method versus data mining
    • Confusion matrices, ROC, and AUC
    • P-values and P-hacking
    • Sampling bias, outliers, and data rot
  4. Autonomous Vehicles
    • Defining autonomous vehicles
    • Levels of autonomy
    • Air and ground vehicle instrumentation
    • Air and ground vehicle automation methods
    • Ego, connected, modular, and end-to-end systems
    • Localization techniques
    • Object detection techniques
    • Environment mapping techniques
    • Human factors in autonomous vehicles
  5. Data Science Talent Strategy
    • Defining data science
    • State of data science as a discipline
    • Rubrics for hiring a data scientist
  6. AI Risk Assessment
  7. AI Hazard Analysis
  8. AI Safety testing/reliability/maintenance

CEU: 3.2

Course Duration: 4.5 Days

Tuition: $3,100 (July 2024 - June 2025); $TBD (July 2025 - June 2026)

Published on May 3rd, 2021

Last updated on July 9th, 2024