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  • Berkeley Global

Artificial Intelligence Foundations

COMPSCI X461

86614611
 

This is a foundational Artificial Intelligence course to help you understand the deep learning frameworks that build upon the underpinning neural network architecture. Artificial Intelligence is pervasive across all domains and can be used for meaningful applications in multiple fields, including cancer detection using MRI scans, autonomous vehicles, speech recognition, weather forecasting and more. You will gain an understanding of versatile AI algorithms such as CNN, RNN, and implement them using frameworks such as keras, pytorch and more.

Prerequisites:

  • Prior knowledge of programming, Python preferred
  • Knowledge of statistics as covered in an undergraduate level course
  • Experience working with machine learning algorithms

Learner Outcomes

Upon completion of this course, students will be able to:

  • Define the field of Artificial Intelligence (AI) and its core principles.
  • Explain the basics of neural networks and their historical development.
  • Differentiate between traditional machine learning and deep learning approaches.
  • Understand the architecture and components of neural networks.
  • Explore optimization techniques, including gradient descent, regularization, and hyperparameter tuning.
  • Implement basic neural network models for supervised learning tasks.
  • Comprehend and apply Convolutional Neural Networks (CNNs) for image data analysis.
  • Implement Recurrent Neural Networks (RNNs) for sequential data processing.
  • Develop autoencoders for unsupervised learning tasks.
  • Differentiate between various types of reinforcement learning algorithms.
  • Implement reinforcement learning techniques for solving control and decision-making problems.
  • Evaluate the strengths and weaknesses of reinforcement learning approaches.
  • Implement AI algorithms for real-world applications, such as image classification, speech recognition, and Natural Language Processing (NLP).
  • Explore the use of AI in recommender systems, autonomous vehicles, and weather forecasting.
  • Analyze the ethical and societal implications of AI applications.
  • Investigate emerging AI techniques, including advanced Transformer models.
  • Explore AI generative algorithms, such as Generative Adversarial Networks (GANs) and ChatGPT.
  • Assess the potential impact of AI advancements on various industries and domains.
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Summer 2025 enrollment opens on March 17!

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This course applies to the following programs:

Advanced Program in Software Development

Expand or collapse section

Programming

  • Java: Discovering Its Power
  • Mastering Python

Back-End Development

  • Back-End Development with Java/Python

Security

  • Web Software Security Frameworks

Electives

  • Software Quality Assurance
  • Software Design Patterns
  • Artificial Intelligence Foundations
  • Advanced Databases

Capstone

  • Advanced Software Development Capstone

Learn More About this Program

Program in Data Science

Expand or collapse section

Databases & Big Data

  • Introduction to Big Data
  • Introduction to Databases

Data Science

  • Introduction to Data Science
  • Introduction to Machine Learning Using Python
  • Machine Learning and Deep Learning
  • Artificial Intelligence Foundations
  • Data Visualization

Data Science Capstone

  • Data Science Capstone

Learn More About this Program


Instructional Types

Live Online
Live Online courses provide an interactive learning experience with scheduled synchronous online sessions held via Zoom video conferencing (Pacific Time). Learn more about the Live Online format.
Online, Start Anytime
Continuous enrollment course begins when you enroll. You have a minimum of 90 days and a maximum of 180 days to complete the course.
Online, Fixed Date
Enroll in this course by its start date and complete it by its specified end date. There are no live sessions, but plenty of opportunities to collaborate with your classmates and instructor.
Classroom
Take classes in-person at one of our buildings or partner locations.

English Language Proficiency Requirements

All of our courses are taught in English. If English is not your first language, please use the following test scores as guides in order to be successful:

  • TOEFL: 90
  • IELTS Academic Format: 7
  • DAAD: C1
  • TEM-4 or TEM-8: Level 70
  • Duolingo: 125

Note: You do not need to submit test scores.

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