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Overview
This training course delves into the application of Artificial Intelligence (AI), with a focus on Machine Learning and Deep Learning, within the automotive industry. Participants will explore current technologies, potential applications, and practical implementations of AI in various automotive scenarios.
Objectives
By the end of this course, leaner will be able to:
- Understand the current state of AI technology in the automotive industry.
- Identify existing AI technologies and their applications within automotive systems.
- Explore potential uses of AI, emphasizing Machine Learning and Deep Learning, for automation and decision-making in cars.
- Gain insight into rules-based AI and its role in simplifying decision-making processes.
- Develop proficiency in Machine Learning techniques such as classification, clustering, and Neural Networks, with a focus on their application in automotive contexts.
Prerequisites
- Basic understanding of Artificial Intelligence concepts.
- Familiarity with Machine Learning and Deep Learning principles.
- Proficiency in Python programming language.
- Knowledge of data preprocessing techniques.
- Understanding of neural network architectures and training methodologies.
Course Outline
Module 1: Current State of AI Technology in Automotive IndustryCourses2024-04-10T18:31:45+05:30
- Overview of AI applications in automotive systems
- Existing technologies and their utilization
- Potential applications of AI in future automotive scenarios
Module 2: Rules-Based AI for Simplifying Decision MakingCourses2024-04-10T18:34:22+05:30
- Understanding rules-based AI and its role in automotive systems
- Simplifying decision-making processes using rules-based approaches
Module 3: Machine Learning Techniques in Automotive IndustryCourses2024-04-10T18:36:30+05:30
- Introduction to Machine Learning
- Classification and clustering algorithms for automotive applications
- Neural Networks and their types relevant to automotive scenarios
Module 4: Deep Learning in Automotive SystemsCourses2024-04-10T18:38:16+05:30
- Basic vocabulary and concepts of Deep Learning
- Determining when to use Deep Learning in automotive applications
- Estimating computational resources and costs for Deep Learning projects
Module 5: Practical Implementation of Deep Learning using TensorFlowCourses2024-04-10T18:42:35+05:30
- Preparing data for Deep Learning models
- Selecting appropriate loss functions and neural network architectures
- Training and evaluating Deep Learning models for automotive tasks
- Sample applications including anomaly detection, image recognition, and Advanced Driver Assistance Systems (ADAS)
