PIC 16 Python with Applications
You can download the course information here.
Student Projects from Past Courses:
Past Projects
Spring 2018 Tentative Course Schedule
Week 1
- 04/02 Lecture 1: Course Overview
– Follow instruction in GettingStarted.pdf on CCLE
– Reading material: Python tutorial 1 and 2.1.2 - 04/04 Lecture 2: Python Basics - Basic Data Types, commenting, list, dictionary, functions, modules
– Reading material: Python tutorial 3.1.1, 3.1.2, 3.1.4, 5.1, 5.5 - 04/06 Lecture 3: Python Basics - Control Flow, Functions
– Reading material: Python tutorial 4.1 - 4.7
Week 2
- 04/09 Lecture 4: Python Basics - Data Structures
– Reading material: Python tutorial 5.2 - 5.5 - 04/11 Lecture 5: Python Basics - Functional Programming
– Reading material: Python tutorial 6.1, 8.1 - 8.4, 5.7 - 04/13 Lecture 6: Python Basics - Classes and Objects, Magic Methods
– Reading material: tutorialspoint
** Homework 1 due Wednesday by 5pm
Week 3
- 04/16 Lecture 7: Regular Expressions I
– Reading material: tutorial
– Cheat sheet
– To test your regular expressions: pythex - 04/18 Lecture 8: Regular Expressions II
- 04/20 Lecture 9: Visualization I: Turtle, Matplotlib and pyplot
– Recursion
– Matplotlib tutorial
** Homework 2 due Wednesday by 5pm
Week 4
- 04/23 Lecture 10: Visualization II: Pandas, Matplotlib and Numpy
– Pandas Tutorial
– Pandas Basics Cheat Sheet (on CCLE) - 04/25 Lecture 11: Visualization III: More on plotting
– Numpy tutorial - 04/27 Lecture 12: GUI I: Tkinter
– An Introduction To Tkinter
** Homework 3 due Wednesday by 5pm
Week 5
- 04/30 Lecture 13: GUI II: Tkinter bouncing ball example
- 05/02 Lecture 14: GUI III: Inheritance
– Executing Modules as Scripts
– Class Inheritance and Overloading Methods - 05/04 Lecture 15: GUI IV: Qt Designer and PyQt
– Qt Designer Documentation
– PyQt5 Tutorial
** Homework 4 due Wednesday by 5pm
Week 6
- 05/07 Lecture 16: Image Processing I: Grey-scale images and adding noise to an image
– Matplotlib image tutorial - 05/09 Lecture 17: Image Processing II: Uniform Blur and Gaussian Blur
– Blurring for Beginners - 05/11 Lecture 19: Page-Rank algorithm
– NetworkX Tutorial
– Read from “Creating a graph” to “Directed graphs”
– PageRank Algorithmk
– (Optional)https://www.geeksforgeeks.org/page-rank-algorithm-implementation
** Homework 5 due Wednesday by 5pm
Week 7
- 05/14 Lecture 18: NLTK
– Read the introduction to Chapter 1 of the NLTK Book.
– Follow 1.2, 1.3, 1.4
– Follow Chapter 1, section 3 (all)
– Skim Chapter 1, section 5. This will give you a good overview of the issues in natural language
– Skim Chapter 3 for processing raw text processing. - 05/16 Lecture 20: Linear algebra and transition matrices
– numpy.linalg - 05/18 Lecture 21: Symbolic Math I
– Sympy Tutorial
** Homework 6 due Wednesday by 5pm
Week 8
-
05/21 Lecture 22: Symbolic Math II, Scipy
– Sympy Tutorial -
05/23 Lecture 23: Machine Learning I: NMF
– Scikit-learn: Non-negative Matrix Factorization -
05/25 Lecture 24: Machine Learning II: KNN and K-means clustering
– Scikit-learn: Nearest Neighbors Classification
– K-nearest Neighbors Wikipedia
– Scikit-learn: K-Means Clustering
– Visualizing K-Means clustering
** Homework 7 due Friday by 5pm
Week 9
- 05/28 Memorial Day holiday (No class)
- 05/30 Lecture 25: Machine Learning III: SVM
– Machine learning: the problem setting
– Scikit-learn: Support Vector Machines (SVMs) - 06/01 Lecture 26: Scrapy
– W3 Schools HTML Intro
– W3 Schools XML Intro
– “What is the DOM” in W3 Schools XML DOM Intro
– XML DOM Nodes
– Scrapy Tutorial
Week 10
-
06/04 Lecture 27: Socket (Networking)
– The first half (16.5 minutes) of Python Advanced Tutorial 6 – Networking
– Socket module documentation
– Python Network Programming
– (Optional) How does ping work? -
06/06 Lecture 28: Catch up
-
06/08 Lecture 29: Final Review
** Homework 8 due Wednesday by 5pm