Schedule #

Important

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

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

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	Jan 8-12	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 15-19	Linear Algebra (L3) (Notes) Prob and Stats (L4) (Notes)		Q1 - L3 & Syllabus Quiz		Correlation vs Covariance; Linear Algebra Review by Zico Kolter;
3	Jan 22-26	Prob and Stats - contd (L5) (Notes) Info Theory (L6) (Notes)	A1 out Jan 23	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 30-Feb 2	Optimization (L7) Toolbox - P2 (L8)		Q3 - L 6,7	Project team composition due Feb 2	KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
5	Feb 5-9	Clustering & K-Means (L9)(Notes) GMM - Part 1 (L10)(Notes)	A1 due Feb 9 A2 out Feb 9	Q4 - L 8,9		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN);
6	Feb 12-16	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 19-23	DB SCAN (L13)(Notes) Clustering Eval (L14)(Notes)		Q6 - L13,14	Project proposal due Feb 23 Peer Evaluation Feb 23	Jupyter Notbook (Kmeans and DBSCAN);
8	Feb 26-Mar 1	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 4-8	Linear Regression (L17)(Notes) LR contd (L18)(Notes)	A2 due Mar 8 A3 out Mar 8	Q8 - L16, L17, L18		Simple Linear Regression in Matrix Format;
10	Mar 11-15	Regularization (L19)(Notes) NB & Logistic Reg (L20)(Notes)		Q9 - L19		Adding Noise to Regression Predictors;
11	Mar 18-22	Spring Break
12	Mar 25-29	NB & Logistic Reg (L21)(Notes) *Project Practical Advice	A3 due March 29 A4 out March 29	Q10 - L20,L21	Project midpoint report due April 2nd Peer Evaluation April 3rd
13	Apr 1-5	Neural Networks (L23)(Notes) NN contd (L24)(Notes)		Q11 - L23,24		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction;
14	Apr 8-12	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 15-19	SVM - Part 1 (L27)(Notes) SVM - Part 2 (L28)(Notes)	A4 Due Apr 19	Q13 - L27,28
16	Apr 22-23	SVM-Kernel (L29)(Notes) *Ethics in ML (L29) Final Class days (Apr 22 and 23)			Final Project due Apr 23 Peer Evaluation Apr 23	KKT and SVM