Schedule #

Important

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

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Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	Jan 12-16	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	Jan 19-23	Linear Algebra (Notes, L3) Prob and Stats (Notes, L4)	A1 out Jan 23	Q1 - L3 & Syllabus Quiz		Correlation vs Covariance; Linear Algebra Review by Zico Kolter;
3	Jan 26-30	Prob and Stats - contd (Notes, L5) Info Theory (Notes, example, L6)		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	Feb 2-6	Info Theory - contd (Notes, L6) Optimization (Notes,L7)		Q3 - L6 (CE and KL slides included)	Project team composition due Feb 6	KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
5	Feb 9-13	Clustering & K-Means (Notes, L8) Data analysis toolbox - P2 (Notes, L9)	A1 due Feb-13 A2 out Feb-13	Q4 - L7,8,9		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN);
6	Feb 16-20	GMM - Part 1 - WILL BE TAUGHT BY DR. MAHDI ROOZBAHANI (Notes L10) GMM - Part 2 - WILL BE TAUGHT BY DR. MAHDI ROOZBAHANI (Notes L11)		Q5 - L10,11		GitHub Student Application
7	Feb 23-27	Hierarchical Clustering (Notes L12) DB SCAN (Notes L13)		Q6 - L12,13	Project proposal due Feb 27 Peer Evaluation Feb 27	Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; Jupyter Notbook (Kmeans and DBSCAN);
8	Mar 2-6	Clustering Eval (Notes L14) Dimension Reduction (Notes L15)	A2 due Mar 6 A3 out Mar 6	Q7 - L14,15		KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
9	Mar 9-13	Linear Regression (Notes L16) LR contd (Notes L17)		Q8 - L16,17		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
10	Mar 16-20	In-Class Assessment on Canvas [3/18] Regularization (Notes L18)		No Quiz	Project midpoint report Apr 20 Peer Evaluation Apr 20	Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
11	Mar 23-27	Spring Break
12	Mar 30- Apr 3	NB & Logistic Reg (Notes L19) NB & Logistic Reg (Notes L20)	A3 due Apr 3 A4 out Apr 3	Q9 - L18, 19,20
13	Apr 6-10	Neural Networks (Notes L22) CNN (Notes L23)		Q10 - L22,23		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction;
14	Apr 13-17	DT and RF (Notes DT & RF, L24) SVM - Part 1 (Notes, L25)		Q11 - L24,25	Project presentation starts from this week (schedule it with your mentor)	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 20-24	SVM - Part 2 (Notes, L26) SVM-Kernel (Notes, L27)	A4 Due Apr 24	Q12 - L26,27		KKT and SVM
16	Apr 27- 28	Focus on project Reading period (Apr 29)			Final Project due Apr 28 Peer Evaluation due Apr 28