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Machine Learning II: Deep Learning and Applications

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Course Objectives

  • Understand machine learning basics
  • Understand deep learning basics such as feedforward neural networks, convolutional neural networks, and recurrent neural networks
  • Know several advanced topics in deep learning, including applications in natural language understanding, graph representation learning, recommender systems, and deep generative models
  • Learn to use PyTorch for applying deep learning techniques to solve real-world problems

Prerequisites

  • Linear algebra
  • Python programming language
  • One of following courses
    • Machine Learning I: Large-scale machine learning and decision making
    • Data Mining

Evaluation

  • Homework: 20%
  • Presentations: 10%
  • Projects: 40% = proposal 5% + poster 10% + report 25%
  • Final Exam: 30%

Staff