Students are expected to learn machine learning algorithms for data analytics and their implementations in Python. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve data-related problems found in the field of mechanical engineering. Starting from basic linear algebra, optimization will be intensively studied. Machine learning algorithms (regression, classification, and clustering) will also be covered with various aspects. Numerical Python coding is heavily asked throughout lectures and homework assignments.

Topic includes Programming in Python, Linear algebra, Optimization, Regression, Classification, Clustering, Statistics, Dimension Reduction, Neural Networks, Autoencoder, etc.

N/A

Attendance (10%) / Homework (20%) / Midterm (30%) / Final Exam (30%) / Project (10%)

6. 강의진도계획(1주 ~ 16주)
Week 1 : Introduction, Linear algebra 1
Week 2 : Linear algebra 2, Optimization
Week 3 : Linear regression 1
Week 4 : Linear regression 2, Perceptron
Week 5 : Support Vector Machine, Logistic regression
Week 6 : kNN, Decision Tree
Week 7 : K-means, Statistics
Week 8 : Midterm
Week 9 : Dimension reduction (PCA, FDA)
Week 10 : Singular Value Decomposition (SVD), Independent Component Analysis (ICA)
Week 11 : From Perceptron to MLP
Week 12 : Artificial Neural Networks
Week 13 : Dimension reduction: Autoencoder
Week 14 : Probability, Gaussian Distribution
Week 15 : Parameter Estimation and Probabilistic Machine Learning
Week 16 : Final exam

Lecture-based