EE370: Software lab.

Course Info.

Course descriptions

• This course covers elementary computational techniques for solving mathematical problems in electrical and electronic engineering. Students will use high level programming languages to formulate, interpret and analyze practical real-world problems encountered at a wide variety of engineering disciplines. Covered topics include but not limited to differential equations, linear algebra, probability, Fourier transform, introductory machine learning and artificial intelligence.

Instructors

• Instructor: Prof. Jong-Han Kim (Rm.516)

• TA: Jun-Hee Kang (Wed class) (Rm.568)

• TA: Kunwoo Park (Thr class) (Rm.337-1)

Lectures

• Wed 13:30-17:20 (Rm.211-1)

• Thr 10:30-14:20 (Rm.211-1)

Office hours

• JHK: Thr 14:30-16:00 (Rm.516), or by appointments.

• TAs: By appointments.

Prerequisites

• Previous exposure to programming languages (Python or others).

Reference textbooks

• There are no required textbooks.

Grading policy

• Final exam (40%)

• Midterm exam (30%)

• Homework assignments and class participation (30%)

Lecture notes

The link directs to the associated Jupyter notebook, which opens on Google Colaboratory when the “Open in Colab” button is clicked.

1. A very short Python introduction

2. Numpy module

3. Matplotlib module

4. Pandas module (Files: kfxsim.csv)

5. Differential equations and dynamical systems

6. Fourier transform and sound signals

7. Bayesian inference

8. Monte-Carlo methods

9. Estimation and filtering

10. Image processing

11. Supervised learning

12. Unsupervised learning

13. Other high level languages (Polynomial fit in python, in julia(.html/.ipynb), in matlab, and the data file fit_data.csv)

Assignments

Assignments will be up with the lab session, during which the students start to work on them. Completed works should be turned in by next week's lecture to the course TAs.

1. Prime counting function (due 9/16, solution)

2. Gauss-Legendre algorithm (due 9/16, solution)

3. Eigenvalues and eigenvectors of symmetric matrices (due 9/23, solution)

4. Google PageRank (due 9/23, solution)

5. Approximations of \(\pi\) function (due 9/30, solution)

6. Baseball statistics (due 10/14, solution)

7. Moving average trend on S&P 500 (due 10/14, solution)

8. Firing table (due 10/21, solution)

9. Back in black (due 10/28)

10. Image noise rejection (due 10/28, solution)

11. Mobile phone localization (due 11/4, solution)

12. Random walk in financial markets (due 11/11)

13. Estimation and filtering (due 11/25, solution)

14. CCTV video processing (Files: vtest.avi) (due 12/2, solution)

15. Power demand prediction (due 12/9)

16. Categorizing FIFA19 soccer players (due 12/16, solution)

Exams

1. Midterm exam (2020 Autumn) (solution)

2. Final exam (2020 Autumn)