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Went over Masaki’s use of 3D plots for zenith-angle normalized plots, etc. He used his own parsing for the date Use of np.meshgrid() and Axes3D() New explanation of basics of linear regression/machine learning. W is the slope b is the intercept X [n x m] 2D matrix of inputs (row = sample; column = data) W [n x 1] 1D column of weights D [m x 1] 1d column of stuff Y = XTW + b*1 (where 1 is a 1 column vector) E = D - Y Error J(W, b) = (1/2m) * ETE = (1/2m) * (|D|2 - DTY - YTD + YTY) = (1/2m) * (|D|2) - (1/m) * DT * (XTW + b*1) + (1/2m)(XWT + b*1T)(XTW + b*1) dJ/dW = 0 and dJ/db = 0 → W = C-1Q dJ/db = (-1/m)DT1 + (1/2m)1T(XTW + l1) + (

Build functions Explanation of math Handout with math and functions and documentation. Functional specification Focus on “legacy” and the base for continuation. Go by “black box” before understanding. PDFs or slides for presentation 1. We want reference for code and the supporting concepts. 2. We want to focus on learning specific algorithms. 3. We want to have material to have a sense of continuation and legacy in a project and also to support understanding and learning. Setup for continuity and focus on code.

Main Future Points Git tutorial? Code + Concept Documentation We want to learn Python in-code documentation Meeting Minutes

Code + Concept Documentation Skeleton/Template: Title Function Title parameters/types What it does Explanation Primary Overview Math explanation Notes/Remarks (optional)