Artificial Intelligence

Course description

Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the computer equipped with AI technology can be even more intelligent than us. The Deep Blue system which defeated the world chess champion is a well-know example.

The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem solving and for reasoning. In fact, the skill for problem solving itself should be acquired through learning. Also, methods for problem solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition.

In this course, we will study the most fundamental knowledge for understanding AI. We will introduce some basic search algorithms for problem solving; knowledge representation and reasoning; pattern recognition; fuzzy logic; and neural networks.

Course objective

The main purpose of this course is to provide the most fundamental knowledge to the students so that they can understand what the AI is. Due to limited time, we will try to eliminate theoretic proofs and formal notations as far as possible, so that the students can get the full picture of AI easily. Students who become interested in AI may go on to the graduate school for further study.

What you’ll learn

  • Understand what is AI, its applications and use cases and how it is transforming our lives
  • Explain terms like Machine Learning, Deep Learning, and Neural Networks
  • Describe several issues and ethical concerns surrounding AI
  • Articulate advice from experts about learning and starting a career in AI

Course Outline

  • Module 1 – Preparatory Sessions – Python & Linux
  • Module 2 – GIT
  • Module 3 – Python with Data Science
  • Module 4 – Advanced Statistics
  • Module 5 – Machine Learning & Prediction Algorithms
  • Module 6 – Data Science at Scale with PySpark
  • Module 7 – AI & Deep Learning using TensorFlow
  • Module 8 – Deploying Machine Learning Models on Cloud
  • Module 9 – Data Visualization with Tableau
  • Module 10 – Data Science Capstone Project
  • Module 11 – Data Analysis with MS Excel
  • Module 12 – Data Wrangling with SQL
  • Module 13 – Natural Language Processing and its Applications