AI for College Students

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About Course

  • Course Overview:
    This course is designed to equip students with the foundational knowledge and practical skills in AI. It covers various AI domains, including machine learning, deep learning, and natural language processing, with a focus on real-world applications.
  • Learning Objectives:
    By the end of this course, students will be able to:
    • Understand the basic concepts and terminology of AI.
    • Implement machine learning algorithms using Python.
    • Develop deep learning models for image and text data.
    • Analyze ethical considerations in AI applications.
    • Create a capstone project demonstrating their AI skills.
  • Who Should Take This Course:
    This course is ideal for college students pursuing degrees in computer science, engineering, data science, or anyone interested in gaining a foundational understanding of AI.
  • Course Structure:
    The course will be delivered through a combination of lectures, hands-on labs, and group projects. Each week will include video lectures, reading materials, and assignments.
  • Tools and Requirements:
    • Basic knowledge of programming (preferably Python)
    • Laptop with internet access
    • Software: Anaconda, Jupyter Notebook, TensorFlow, PyTorch
  • Preparation:
    Students are encouraged to review basic programming concepts and familiarize themselves with Python before the course begins.
  • Support:
    Students will have access to discussion forums, weekly Q&A sessions, and one-on-one support from instructors.
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What Will You Learn?

  • Fundamental concepts of AI and its applications
  • Machine learning algorithms and their implementation
  • Deep learning techniques and frameworks
  • Natural language processing and its use cases
  • Ethical implications of AI technologies

Course Content

Introduction to AI
• History of AI • Types of AI: Narrow vs. General AI • Applications of AI in various industries

Machine Learning Basics
• Supervised vs. Unsupervised Learning • Key algorithms: Linear Regression, Decision Trees, K-Means Clustering • Hands-on: Implementing a simple ML model

Deep Learning Fundamentals
• Introduction to Neural Networks • Activation functions and loss functions • Hands-on: Building a neural network using TensorFlow

Natural Language Processing (NLP)
• Text preprocessing techniques • Sentiment analysis and text classification • Hands-on: Building a simple chatbot

Computer Vision
• Image processing basics • Convolutional Neural Networks (CNNs) • Hands-on: Image classification project

AI Ethics and Societal Impact
• Ethical considerations in AI • Bias in AI algorithms • Case studies on AI impact

AI Tools and Frameworks
• Overview of popular AI tools • Hands-on: Using PyTorch for deep learning

Capstone Project and Presentations
• Students will work on a project of their choice • Presentations to showcase their work

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