Lectures

Cogs 109 SS1_23

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Click the links in the topics column to download pdf of lecture notes.

Current week is highlighted in yellow. Topics may change slightly (or order of topics).

I'll do my best to post the slides before lecture, but if not possible I will post at the beginning of lecture either to canvas or github - historically in that room canvas works better for some reason. I had the opposite with another class in a different room.

Week	Lecture #	Date	Topics	Description	Section topics	Assignments
wk1	1	7/4	No class - 4th of july	--	--	--
wk1	2	7/4	No class - 4th of july	--	--	--
wk1	3	7/6	Lecture 1 L1_Podcast	Introductory lecture and welcome to COGS109!
wk1	4	7/6	Lecture 2 L1_Podcast	Version control, git, github and relevance to modeling and data analysis	Introduction, D1, A0, accessing course resources D1slides	Assigned: D1, A0 (Practice), LQ1
wk2	5	7/11	Lecture 3 L2_Podcast	Data structures, types, binary vs. ASCII, how to load data into Python using python core and Pandas, Matlab/Octave, converting and associated issues
wk2	6	7/11	Lecture 4 L2_Podcast	Paper review Groups discussion How to read a scientific paper Filtering, discretization, sampling, aliasing	D2slides, D2, A1 Github review git review command line review, EDA and Visualization	Assigned: D2, Paper review
wk2	7	7/13	Lecture 5 L3_Podcast	More on filtering, discretization, sampling, aliasing In class workbook on data importing (available here)
wk2	8	7/13	Lecture 6 L3_Podcast	Perceptually aware visualization	D2 completion Math Review, prep for A1	Assigned: D3, A1, Proposal, LQ2
wk3	9	7/18	Lecture 7 L4 Podcast	Data Analysis I: Central tendency
wk3	10	7/18	Lecture 8 L4 Podcast	Data Analysis II: Variability	Project discussion	Assigned: D4
wk3	11	7/20	Lecture 9 L5_Podcast	Hypothesis testing as it pertains to modeling and data analysis
wk3	12	7/20	Lecture 10 L5_Podcast	Newton vs. Palm and modeling/data analysis Colormap design Interpolation, LERP, BERP, TERP, SLERP	Project Q&A Overview of descriptive stats Discussion Q&A	D5, A2, CP1
wk4	13	7/25	Lecture 11 L6_Podcast Least squares.pdf Least_squares.ipynb	Modeling and Data Analysis big picture and pathway Review of expanded filtering notebook Regression - PDE approach, Jupyter examples
wk4	14	7/25	Lecture 12 L6_Podcast Lagrange paper	Nonlinear regression with least squares Lagrange interpolation Splines, piecewise continuous functions Orion Nebula example Error analysis introduction	Regression and error analysis	D6, LQ3
wk4	15	7/27	Lecture 13 L7_Podcast	Optimization, Nelder-Mead Simplex, gradient descent, conjugate gradient
wk4	16	7/27	Lecture 14 L7_Podcast	Perceptrons, Threshold logic unit, Artificial Neural Networks		D7, A3 (cancelled), CP2, LQ4
wk5	17	8/1	Lecture 15 L8_Podcast	ANN II supervised and unsupervised learning
wk5	18	8/1	Lecture 16 L8_Podcast	AI, heuristics, search, decision trees	Project meetings
wk5	19	8/3	Lecture 17 L9_Podcast	Big picture of modeling and data analysis, case studies, examples
wk5	20	8/3	Lecture 18 L9_Podcast	Where to go from here, the conclusion, final thoughts and advice	D7 and conclusion	Final Project