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

Astericks (*) indicate that the lecture slides/notes are available.

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	Jan 6-10	Course Overview (L1) Data analysis toolbox - P1 (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	Jan 13-17	Linear Algebra (L3) (Notes) Prob and Stats (L4) (Notes)	A1 out Jan 17	Q1 - L3 & Syllabus Quiz		Correlation vs Covariance; Linear Algebra Review by Zico Kolter;
3	Jan 20-24	Prob and Stats - contd (L5) (Notes) Info Theory (L6) (Notes)		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	Jan 27-31	Optimization (L7) Toolbox - P2 (L8)		Q3 - L 6,7	Project team composition due Jan 31	KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
5	Feb 3-7	Clustering & K-Means (L9)(Notes) GMM - Part 1 (L10)(Notes)	A1 due Feb 7 A2 out Feb 8	Q4 - L 8,9		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN);
6	Feb 10-14	GMM - Part 2 (L11)(Notes) Hierarchical Clustering (L12) (Notes)		Q5 - L10,11,12		Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
7	Feb 17-21	DB SCAN (L13)(Notes) Clustering Eval (L14)(Notes)		Q6 - L13,14	Project proposal due Feb 21 Peer Evaluation Feb 21	Jupyter Notbook (Kmeans and DBSCAN);
8	Feb 24-28	Density Estimation (L15)(Notes) Dimension Reduction (L16)(Notes)		Q7 - L15		KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example; Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
9	Mar 3-7	Linear Regression (L17)(Notes) LR contd (L18)(Notes)	A2 due Mar 7 A3 out Mar 8	Q8 - L16, L17, L18		Simple Linear Regression in Matrix Format;
10	Mar 10-14	Regularization (L19)(Notes) NB & Logistic Reg (L20)(Notes)		Q9 - L19		Adding Noise to Regression Predictors;
11	Mar 17-21	Spring Break
12	Mar 24-28	NB & Logistic Reg (L21)(Notes) *Project Practical Advice	A3 due March 28 A4 out March 29	Q10 - L20,L21
13	Mar 31- Apr 4	Neural Networks (L23)(Notes) NN contd (L24)(Notes)		Q11 - L23,24	Project midpoint report due March 31 Peer Evaluation April 1st	NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction;
14	Apr 7-11	CNN (L25) (Notes) DT and RF (L26)		Q12 - L25,26		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	Apr 14-18	SVM - Part 1 (L27)(Notes) SVM - Part 2 (L28)(Notes)	A4 Due Apr 18	[Q13 – L27,28 (same deadline as previous quizzes) Q14 – L29 (deadline April 22; you may take Q14 without penalty until Apr 24)]
16	Apr 21-22	SVM-Kernel (L29)(Notes) *Ethics in ML (L30) Final Class days (Apr 21 and 22)			Final Project due Apr 22 Peer Evaluation Apr 22	KKT and SVM