Links to slides and Jupyter notebooks used during lectures,  as well as  assignments, will be posted here throughout the quarter.

Date                   Session
03/29                Foundations What is Data? What are Models?
03/31                Foundations Modeling: a framework and strategy
04/05                Foundations Reliability: the data as a model of itself
04/07                Foundations Regression: OLS
04/12                Foundations Correlation Analysis
04/14                Supervised Models Minimum distance classifiers
04/19                Supervised Models SVMs and logistic regression
04/21                Supervised Models Regularization
04/26                Supervised Models Mixed effects and hierarchical models
04/28                Unsupervised Models Clustering
05/03                Unsupervised Models Linear Algebra Review


05/05                Unsupervised Models Dimensionality Reduction
05/10                Timeseries Models Guest lecture from Nilam Ram
05/12                Causal Models Structural Equation Modeling

05/17                Causal Models Graphical Models

05/19                Causal Models Network Analysis
05/24                Foundations Optimization
                                  Calculus Review
                                  Intro to Tensorflow
05/26                Foundations Making Your Own Custom Models
05/31                Memorial Day Holiday - no class
06/02                Project Presentations

ipynb
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Lecture 9
Lecture 10
Lecture 11a (Vectors)
Lecture 11b (Matrices)
Lecture 11c (Eigenstuff)
Lecture 12

Lec. 14a (SEM)
Lec. 14b (Indirect Effects)
Lec. 15a (Graphical Models)
Lec. 15b (Gaussian GMs)
Lecture 16
Lec. 17 (Optimization)
Lec. 17a (Calc. Review)
Lec. 17b (Tensorflow)
Lecture 18

Assignments
HW1 Released




HW1 Due; HW2 released








HW2 Due










Project writeup due