Syllabus

• syllabus pdf

• Canvas class site

Course Materials

• All lecture videos are here, accessible with your CU account.

• All lecture notes are in a Onenote.

• The all-in-one lecture notes in one pdf, including the homework problems.

• Homework solutions are under \Files in Canvas

Homework (Due every Thursday)

• Homework should be submitted to Canvas.

• For homeworks with programming, submit both Jupyter notebooks and pdfs of the notebooks. (In Google Colab, you can print the pdf by selecting everything in the notebook and printing it to a pdf.)

1. 1.2.A.1 (programming) in the above all-in-one lecture notes pdf

2. 1.2.A.2., read run the code of [Raschka, Ch 2 and 3]. (If you cannot obtain the textbook in time, you can read the slides and watch videos. See Section 1.1.4.0.3. of my pdf notes for resources of [Raschka].)

3. Study available materials (book, slides, videos) and run code of [Raschka Ch 7] (no need to turn in)

4. 1.7.A.1 (programming, submit both your Jupyter Notebook and a print of it in pdf), Read Sections 12.10 and 12.11 of https://d2l.ai/  to get an idea on real world SGD algorithm for neural networks.

5. 1.10.A.1., Study available materials (book, slides, videos) and run code of [Raschka Ch 3, Kernel SVM] (no need to turn in)

6. 1.11.A.1, ([Raschka Ch 5], study available materials (book, slides, videos) and run codes, no need to turn in)

7. A link to the homework is in Canvas hw07. Copy the Jupyter notebook there, provide your solution in it, save it. To print to a pdf, click File and select Print in Google Colab. Submit your pdf and Jupyter notebook to Canvas.

Projects

1. The link to the Project 1, 2 assignment folder is in Project 1, 2 on Canvas