Schedule #

Important

All deadline and due dates in this course will be at 23:59 EST.

Scroll horizontally to see the full schedule table on mobile devices

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	Aug 18-22	Course Overview (Notes,L1) Data analysis toolbox - P1 (Notes,L2)		Q0 - L1,2 (Warmup)		GT Honor Code; Debugging Common Errors in NumPy; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; Overleaf for GT students;
2	Aug 25-29	Linear Algebra (Notes, L3) Prob and Stats (Notes, L4)		Q1 - L3 & Syllabus Quiz		Correlation vs Covariance; Linear Algebra Review by Zico Kolter;
3	Sep 1-5	Prob and Stats - contd (Notes, L5) Info Theory (Notes, L6)	A1 out Sep 2	Q2 - L4,5		The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore;
4	Sep 8-12	Info Theory - contd (Notes, L6) Optimization (Notes, L7)		Q3 - L6 (CE and KL slides included)	Project team composition due Sep 12	KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
5	Sep 15-19	Toolbox - P2 (L8) Clustering & K-Means (Notes L9)	A1 due Sept-19 A2 out Sept-19	Q4 - L7,L8 (includes toolbox 1),L9		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN);
6	Sep 22-26	GMM - Part 1 (Notes L10) GMM - Part 2 (Notes L11)		Q5 - L10,11		GitHub Student Application
7	Sep 29-Oct 3	Hierarchical Clustering (Notes L12) DB SCAN (Notes L13)		Q6 - L12,13	Project proposal due Oct 3 Peer Evaluation Out on Oct 3	Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; Jupyter Notbook (Kmeans and DBSCAN);
8	Oct 6-10	Fall Break (Oct 6-7) Clustering Eval (Notes L14) No class on Oct 10		Q7 - L14 (out 10/9 and same due as others)	Peer Evaluation Due on Oct 10	KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
9	Oct 13-17	Dimension Reduction (Notes L15) In-Class Assessment on Canvas	A2 due Oct 17 A3 out Oct 17	Q8 - L15		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
10	Oct 20-24	Linear Regression (Notes L16) LR contd (Notes L17)		Q9 - L16,17		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
11	Oct 27-31	Regularization (Notes L18) NB & Logistic Reg (Notes L19)		Q10 - L18
12	Nov 3-7	NB & Logistic Reg (Notes L20) Project Practical Advice		Q11 - L19,20	Project midpoint report Nov 7 Peer Evaluation Nov 7
13	Nov 10-14	Neural Networks (Notes L22) CNN (Notes L23)	A3 due Nov 14 A4 out Nov 14	Q12 - L22,23		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction;
14	Nov 17-21	DT and RF (Notes DT & RF, L24) SVM (Notes, L25)		Q13 - L24		CNN Live Demo; A guide to an efficient way to build CNN and optimize its hyper-parameters; Back Propagation in CNN; Transfer learning in CNN; Project Scoring Guidance;
15	Nov 24-28	SVM-Kernel- recorded video on Canvas (Notes, L26) Student Recess and Thanksgiving break (Nov 27, 28 and 29)		Q14 - L25,26 (will be available till Dec 2 with no penalty)
16	Dec 1-5	Reading period (Dec 3-4)	A4 Due Dec 2		Final Project due Dec 2 Peer Evaluation due Dec 2	KKT and SVM