Course Schedule

[Mon] (Jan 20)

[Wed] Reproducibility 1 (Jan 22)

• Course Overview
• Hardware, OS, Interpreters

[Fri] Reproducibility 2 (Jan 24)

• versioning
• git

[Mon] Quantifying Perf 1 (Jan 27)

• check_output
• time

[Wed] Quantifying Perf 2 (Jan 29)

• complexity analysis
• Big O

[Fri] Quantifying Perf 3 (Jan 31)

• practice

[Mon] OOP 1: Classes (Feb 3)

• attributes
• methods
• constructors

[Wed] OOP 2: Special Methods (Feb 5)

• __str__, __repr__, _repr_html_
• __eq__, __lt__
• __len__, __getitem__
• __enter__, __exit__

[Fri] OOP 3: Inheritance (Feb 7)

• method resolution order
• overriding methods
• calling overridden methods

[Mon] Review + Practice (Feb 10)

• Inheritance
• Recursion

[Wed] Graphs 1 (Feb 12)

• review recursion
• types of graph
• graphviz

[Fri] Graphs 2 (Feb 14)

• trees
• binary trees
• binary search trees (BSTs)

[Mon] Graphs 3 (Feb 17)

• implementing dictionaries with BSTs
• graph search

[Wed] Graphs 4 (Feb 19)

• DFS vs. BFS
• time vs. memory complexity

[Fri] Graphs 5 (Feb 21)

• dense graphs
• Markov chains
• log likelihood

[Mon] Regex 1 (Feb 24)

• character classes
• repetition
• anchoring
• findall, sub, groups

[Wed] Catchup (Feb 26)

• regex

[Fri] Web 1: Flask (Feb 28)

• review from CS 220
• RPC, POST
• decorators

[Mon] Web 2: Tracing (Mar 2)

• wrappers based on *args and **kwargs

[Wed] Web 3: Selenium (Mar 4)

• advanced web scraping

[Fri] Web 4: Catchup (Mar 6)

[Mon] Web 5: Recursive Crawl (Mar 9)

• BFS for webpages
• robots.txt
• HTTP 429

[Wed] Review/Exam (Mar 11)

[Fri] Web 5: A/B testing (Mar 13)

• data collection
• significance

[Mon] Spring Break! (Mar 16)

[Wed] Spring Break! (Mar 18)

[Fri] Spring Break! (Mar 20)

[Mon] Visualization 1 (Mar 23)

• wrapup web scraping
• matplotlib coordinate systems

[Wed] Visualization 2 (Mar 25)

• drawing custom lines/polygons
• geographic maps

[Fri] Visualization 3 (Mar 27)

• shapefiles
• shapely
• high resolution graphics

[Mon] Visualization 4 (Mar 30)

• animations

[Wed] Linear Algebra 1 (Apr 1)

• machine learning overview
• numpy arrays
• image manipulation

[Fri] Linear Algebra 2 (Apr 3)

• matrix multiplication basics
• equation solving

[Mon] Linear Algebra 3 (Apr 6)

• linear combinations
• column spaces
• projection matrices

[Wed] Linear Algebra 4 (Apr 8)

• fit lines
• loss functions

[Fri] Linear Algebra 5 (Apr 10)

• fitting non-linear data
• correlation matrices

[Mon] Linear Algebra 6 (Apr 13)

• principal component analysis

[Wed] Parallelism 1 (Apr 15)

• multiprocessing pools
• parallel map

[Fri] Parallelism 2 (Apr 17)

• pytorch

[Mon] Optimization (Apr 20)

• gradient descent
• least squares

[Wed] Supervised Learning 1 (Apr 22)

• classification
• train+test split
• sklearn Pipeline

[Fri] Supervised Learning 2 (Apr 24)

• confusion matrices
• precision and recall
• multi-class logistic regression

[Mon] Supervised Learning 3 (Apr 27)

• categorical inputs
• one-hot encoding

[Wed] Unsupervised Learning 1 (Apr 29)

• k-means

[Fri] Unsupervised Learning 2 (May 1)

• k-means limitations
• agglomerative clustering
• dendograms