2025 spring

Machine Learning (2 Div)

This course covers theoretical backgrounds and practical implementation of different machine learning techniques, including supervised learning, unsupervised learning, deep learning, active learning, anmd reinforcement learning. In addition, it explores the entire pipeline to build applications of machine learning with practices. Furthermore, it provides a broad introduction to ethical issues relevant to machine learning.

Instruction

Course Staff
Time & Location
  • Tue./Wed. 09:00 - 10:45, #608, College of Engineering #6
Office Hours
  • Tue. 13:00 - 15:00
Textbook
  • Primary
    • [Ge23] Aurélien Géron. 2023. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3rd Ed. O`Reilly
  • Secondary
    • [Oh21] 오일석. 2021. 기계 학습. 한빛 아카데미
    • [Ow22] Louis Owen. 2022. Hyperparameter Tuning with Python: Boost your machine learning model’s performance via hyperparameter tuning. Packt.
    • [Br20] Jason Brownlee. 2020. Data Preparation for Machine Learning, 1.1 Ed. Machine Learning Mastery
    • [Br21] Jason Brownlee. 2021. Imbalanced Classification with Python, 1.3 Ed. Machine Learning Mastery
    • [Mo24] Christoph Molnar. 2024. Interpretable Machine Learning
    • [Ma23] Serg Masis. Interpretable Machine Learning with Python. 2023
Prerequisite
  • (Mandatory) Python Programming, Data Analysis Programming
    • All materials were prepared assuming students were proficient in Python programming and familiar with Numpy and Pandas.
Grading Policy
Data Collection (15%)
  • Collect sensor data for human activity recognition
Individual ML Competitions via Kaggle (75%)
  • Round 0 - Being Familiar with Kaggle (5%)
  • Round 1 - TBD (8%)
  • Round 2 - TBD (8%)
  • Round 3 - TBD (8%)
  • Special Round - TBD (15%)
  • Round 4 - TBD (8%)
  • Round 5 - TBD (8%)
  • Final Round - Human Activity Recognition (15%)
Attendance (10%)
  • 1% of credit is deducted for each absence
  • 3-Lateness = 1-Absence
  • 11-Absence = F grade

Schedule

Week 01
March 04: Overview
March 05: Machine Learning Landscape
  • Lecture
  • Reference
    • [Ge23] Chap. 1
    • [Oh21] Chap. 1
Week 02
March 11: Machine Learning Pipeline
  • Lecture
  • Reference
    • [Ge23] Chap. 2
    • D. Sculley et al. 2015. Hidden technical debt in Machine learning systems. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (NIPS'15).
    • Soowon Kang et al. 2023. K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels. Sci Data 10, 351 (2023).
March 12: End-to-End Practice for Machine Learning Pipeline
Week 03
March 18: Linear Model
March 19: Support Vector Machine
  • Lecture
  • Lab
  • Reference
    • [Ge23] Chap. 5
    • [Oh21] Chap. 11
Week 04
March 25: Performance Measures
March 26: Cross-Validation
  • Lecture
  • Lab
  • Reference
    • Berrar, D. 2019. Cross-Validation
Week 05
April 01: Decision Tree
April 02: Ensemble Learning Basics & Random Forest
  • Lecture
  • Lab
  • Reference
    • [Ge23] Chap. 7
    • [Oh21] Chap. 12
Week 06
April 08: Gradient Boosting
April 09: Feature Extraction
  • Lecture
  • Lab
  • Reference
    • Andreas Bulling et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46, 3, Article 33.
    • Soujanya Poria et al. 2017. A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98–125.
Week 07
April 15: Feature Selection
April 16: Dimensionality Reduction
  • Lecture
  • Lab
  • Reference
    • [Ge23] Chap. 8
    • [Br20] Chap. 7
Week 08
April 22: Balancing Label Distribution
April 23: Focus on the Individual Assigment!
  • No Class
Week 09
April 29: Hyper-parameter Tuning #1
April 30: Hyper-parameter Tuning #2
  • Lecture
  • Lab
  • Reference
    • [Ow22] Chap. 5, 6, 9, 10
    • Tong Yu and Hong Zhu. 2000. Hyper-Parameter Optimization: A Review of Algorithms and Applications
Week 10
May 06: Substitution Holiday for Children's Day
  • No Class
May 07: Clustering
  • Lecture
  • Lab
  • References
    • [Ge23] Chap. 9
    • [Oh21] Chap. 6
Week 11
May 13: Artificial Neural Network
May 14: Deep Neural Network
  • Lecture
  • Lab
  • References
    • [Ge23] Chap. 11
    • [Oh21] Chap. 4, 5
Week 12
May 20: Convolution Neural Network
  • Lecture
  • Lab
  • References
    • [Ge23] Chap. 14
    • [Oh21] Chap. 4
May 21: Recurrent Neural Network
  • Lecture
  • Lab
  • References
    • [Ge23] Chap. 15
    • [Oh21] Chap. 8
Week 13
May 27: Autoencoder
May 28: Generative Models
Week 14
June 03: Attention and Transformer
  • Lecture
  • Lab
  • References
    • Ashish Vaswani et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems
  • (Announce) Team Assignment #2: TBD
    • Due: June 16
June 04: Interpretability
  • Lecture
  • Lab
  • References
    • [Mo24] Chap. 5 - 10
    • [Ma23] Chap. 4 - 8
Week 15
June 10: Reinforcement Learning
June 11: Focus on the Team Assigment!
  • No Class
Week 16
June 17: Final Remark