PIC 16B Python with Applications II

You can download the course information here.

Spring 2021 Tentative Course Schedule

Week 1

• 03/29 Lecture 1: Welcome and Course Overview
  – Follow instructions in GettingStarted_PIC16B.pdf on CCLE
  

• 03/31 Lecture 2: Data manipulation: joining and reshaping
  – Seaborn
  – Pandas
  

• 04/02 Lecture 3: Databases I: SQLite
  – SQLite
  

Week 2

• 04/05 Lecture 4: Databases II: SQlite
  

• 04/07 Lecture 5: Advanced Data Analysis I
  – SQLite Tutorial
  – Seaborn
  – Seaborn cheatsheet on CCLE
  

• 04/09 Lecture 6: Advanced Data Analysis II

** Homework 1 due Friday by 5pm

Week 3

• 04/12 Lecture 7: Advanced visualization: plotly
  – Plotly: Getting Started with Plotly in Python
  – Plotly Python Graphing Library
  

• 04/14 Lecture 8: Web Scraping I
  – W3 Schools HTML Intro
  – W3 Schools XML Intro
  – “What is the DOM” in W3 Schools XML DOM Intro
  – XML DOM Nodes
  – Scrapy Tutorial
  

• 04/16 Lecture 9: Web Scraping II
  

Week 4

• 04/19 Lecture 10: Linear algebra
  – NumPy Linear Algebra
  – Array Broadcasting
  

• 04/21 Lecture 11: Intro to Machine Learning
  – Machine learning: the problem setting
  – Nearest Neighbors
  

• 04/23 Lecture 12: Optimization and Neural Networks
  

** Homework 2 due Monday by 5pm

Week 5

• 04/26 Lecture 13: Neural Network + Tensorflow
  – TensorFlow for beginner
  – Regression with TensorFlow
  

• 04/28 Lecture 14: Tensorflow and Keras
  – TensorFlow for beginner
  – Regression with TensorFlow
  

• 04/30 Lecture 15: Tensorflow - CNN
  – Image Classification with TensorFlow.

** Homework 3 due Monday by 5pm

Week 6

• 05/03 Midterm exam

• 05/05 Lecture 16: Tensorflow - CNN ctd
  

• 05/07 Lecture 17: Tensorflow - autoencoder
  – “Classification on imbalanced data” tutorial
  – “Intro to Autoencoders” tutorial
  

Week 7

• 05/10 Lecture 18: Machine learning Interpretability - logistic regression
  – Interpretable Machine Learning 4.1, 4.2 by Christoph Molnar
  – imbalanced-learn please install this package to your pic16b environment.
  

• 05/12 Lecture 19: Machine learning Interpretability - PDP and ICE
  – Scikit-Learn: Partial Dependence Plots
  – Interpretable Machine Learning 5.1, 5.2 by Christoph Molnar
  – (optional)Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation by Goldstein et al.
  – (optional)PyCEbox
  

• 05/14 Lecture 20: Machine learning Interpretability - SHAP
  – Interpretable Machine Learning 5.9 by Christoph Molnar
  – SHAP package: please install this to your pic16b environment.
  

** Homework 4 due Wednesday by 5pm

Week 8

• 05/17 Lecture 21: Network analysis
  – Networkx Tutorial
  

• 05/19 Lecture 22: Network analysis II

• 05/21 Lecture 23: Symbolic Math
  – SymPy Tutorial
  

** Homework 5 due Wednesday

Week 9

• 05/24 Lecture 24: SciPy
  –SciPy library tutorials
  

• 05/26 Lecture 25: Computer vision I
  

• 05/28 Lecture 26: Computer vision II
  

** Homework 6 due Friday

Week 10

• 05/31: Memorial Day; No class

• 06/02 Lecture 27: Multi-threading
  

• 06/04 Lecture 28: Networking - Socket
  – The first half (16.5 minutes) of Python Advanced Tutorial 6 – Networking
  – Socket module documentation
  – Python Network Programming
  – (Optional) How does ping work?
  

Project Due: June 4, 2021