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Machine Learning �for English Analysis

Prof. Seungtaek Choi

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Today

  • Course overview
  • Introduction to AI
  • Git/GitHub Basics

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Course Overview (1)

  • Course title: 영어분석을위한기계학습
  • Instructor: Prof. Seungtaek Choi
    • Office: 교수회관 435호
      • Officie Hour: 금요일 오후 3시-6시 (only by appointment)
      • Email: seungtaek.choi@hufs.ac.kr

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Course Overview (2)

  • Grading (tentative)
    • Midterm exam (35%)
    • Final exam (35%)
    • Attendance and Participation (10%)
    • Assignments (20%)
      • planned like a semester project!

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Course Overview (3)

  • Lecture materials will be uploaded via the lecture page!
    • Link: https://hist0613.github.io/teaching/2025-2/HUFS-LAI-ML4E/
    • I will be using slide materials available on the web from different people. I will acknowledge them whenever necessary.

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Make lecture as interactive as possible

  • Lecture materials will be interactively improved
    • So, please feel free to ask any question
  • Curriculum can also be updated based on feedback
    • So, please feel free to request any support regarding ML

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What are anticipated

  • To acquire core knowledge of machine learning and deep learning
  • To practice data processing skills and fundamental principles

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Course Policies (1)

  • Collaboration
    • may collaborate with anyone
    • required to write code independently and write names of all collaborators on submission
    • we will may run a code similarity program on all problem sets

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Course Policies (2)

  • You are welcome to use AI—but use it to build your own ability, not just to get the assignment done. If you skip doing the work yourself, the exams will be very tough.
  • Learn by reading others’ code through PR reviews or merged PRs: ask about intent, discuss, and absorb ideas. Still, attempt the assignment yourself first; that struggle is what hardens your foundations for the future.
  • Always credit any help (people, AI, external code, etc.) in your PR description or inline comments.
  • If you’re curious about anything, please ask—on GitHub Issues or the eClass Q&A/Open board—and don’t hide your questions so that your knowledge can help your classmates, too.

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Prepare with AI (1)

  • Ask to AI
    • This would greatly improve your understanding in this class!

​

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Prepare with AI (2)

​

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Introduction to AI

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What is Artificial Intelligence (AI)?

Some possible definitions:

  • Thinking humanly
  • Acting humanly
  • Thinking rationally

Ref: https://arxiv.org/abs/2109.01517

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Thinking humanly

  • Cognitive science: the brain as an information processing machine
    • Requires scientific theories of how the brain works
  • How to understand cognition as a computational process?
    • Introspection: try to think about how we think
    • Predict and test behavior of human subjects
    • Image the brain, record neurons
  • The latter two methodologies are the domains of cognitive science and cognitive neuroscience

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Acting humanly

  • Turing (1950) “Computing machinery and intelligence”
  • The Turing Test

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  • What capabilities would a computer need to have to pass the Turing Test?
    • Natural language processing, knowledge representation, automated reasoning, machine learning, …

​

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Thinking rationally

  • Idealized or “right” way of thinking
  • Logic: patterns of argument that always yield correct conclusions when supplied with correct premises
    • “Socrates is a man; all men are mortal; therefore Socrates is mortal.”
  • Beginning with Aristotle, philosophers and mathematicians have attempted to formalize the rules of logical thought
  • Logicist approach to AI: describe problem in formal logical notation and apply general deduction procedures to solve it
  • Problems with the logicist approach
    • Computational complexity of finding the solution
    • Describing real-world problems and knowledge in logical notation
    • Dealing with uncertainty
    • A lot of intelligent or “rational” behavior has nothing to do with logic

​

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AI Connections

Philosophy → logic, methods of reasoning, mind vs. matter, foundations of learning and knowledge

Mathematics → logic, probability, optimization

Economics → utility, decision theory

Neuroscience → biological basis of intelligence

Cognitive science → computational models of human intelligence

Linguistics → rules of language, language acquisition

Machine learning → design of systems that use experience to improve performance

Control theory → design of dynamical systems that use a controller to achieve desired behavior

Computer engineering, mechanical engineering, robotics, …

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What are some successes of AI today?

  • Just few years ago…
    • Language modeling (a.k.a. Chatbot)

Just few years ago…

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What are some successes of AI today?

  • Just few years ago…
    • Language modeling (a.k.a. Chatbot)
  • Recently…
    • Large language models (LLMs): ChatGPT, Gemini, Claude, DeepSeek, Qwen, Mistral, EXAONE, Solar, HyperCLOVA, …

Just few years ago…

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What are some successes of AI today?

  • Just few years ago…
    • Video generation

Just few years ago…�Will Smith Eating Spaghetti test

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What are some successes of AI today?

  • Just few years ago…
    • Video generation
  • Recently…
    • Video generation

Just few years ago…�Will Smith Eating Spaghetti test

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What are some successes of AI today?

  • Just few years ago…
    • Video generation
  • Recently…
    • Video generation

https://www.reddit.com/r/singularity/comments/1krgm19/veo_3_standup_comedy/

Just few years ago…�Will Smith Eating Spaghetti test

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What are some successes of AI today?

  • Self-driving cars (Tesla, …)

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What are some successes of AI today?

  • Speech technologies

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What are some successes of AI today?

  • OCR, visual search, …

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What are some successes of AI today?

  • Game

(May 1997)

IBM’s Deep Blue

(Dec 2013)

DeepMind’s Atari

(Mar 2016)

DeepMind’s AlphaGo

(Aug 2025)

DeepMind’s Genie 3

https://www.britannica.com/topic/Deep-Blue

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What are some successes of AI today?

  • Game

(May 1997)

IBM’s Deep Blue

(Dec 2013)

DeepMind’s Atari

https://www.youtube.com/watch?v=TmPfTpjtdgg

(Aug 2025)

DeepMind’s Genie 3

(Mar 2016)

DeepMind’s AlphaGo

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What are some successes of AI today?

  • Game

(May 1997)

IBM’s Deep Blue

(Dec 2013)

DeepMind’s Atari

https://www.bbc.com/news/technology-35785875

(Aug 2025)

DeepMind’s Genie 3

(Mar 2016)

DeepMind’s AlphaGo

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What are some successes of AI today?

  • Game

(May 1997)

IBM’s Deep Blue

(Dec 2013)

DeepMind’s Atari

https://www.youtube.com/watch?v=PDKhUknuQDg

https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/

(Aug 2025)

DeepMind’s Genie 3

(Mar 2016)

DeepMind’s AlphaGo

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What are some successes of AI today?

  • Robotics

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Git/GitHub Basics

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Or, How to Submit Assignment!

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Lecture Repository

  • Link: https://github.com/HUFS-LAI-Seungtaek/HUFS-LAI-ML4E-2025-2
  • You need to …
    • 1. fork lecture repository
    • 2. commit your files to “your” repository
    • 3. submit PR to lecture repository

​

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What is Git?

  • Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

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What is GitHub?

  • GitHub is a cloud-based platform built on the "Git" version control system that provides tools for developers to store, manage, share, and collaborate on code and other files.

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Basics of Git

  • Repository is a version-controlled project space that stores your files, branches, and full change history.
  • Branch is an independent line of development – a named timeline of commits within a repo.
  • Commit is a saved snapshot of changes with a message, author, and timestamp.

main

main

HUFS-LAI-Seungtaek/HUFS-LAI-OOP-2025-2:main

hist0613/HUFS-LAI-OOP-2025-2:main

main+1

main.py

main+2

test.py

main+2

main+1

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Fork repository

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Fork repository

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Repo is copied under your account.

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Add a file

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Add a file

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Add a file

members/{학생이름}.md

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Introduce yourself

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Commit the change (your file)

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Back to “your” repo

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Submit PR to lecture repository

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Submit PR to lecture repository

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Submit PR to lecture repository

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1st Assignment!

  • Assignment #1: Write your introduction and submit PR

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Next

  • Machine Learning Taxonomy of Problems
  • Regression