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	5/18 - 5/22	Course Overview (Notes, L1) Linear Algebra (Notes, L2)		Q0 - L1 \| Q1 - L2		GT Honor Code; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; Overleaf for GT students; Linear Algebra Review by Zico Kolter;
2	5/25 - 5/29	Memorial Day Holiday Prob and Stats (Notes, L3)	A1 out 5/25	Q2 - L3 \| Syllabus Quiz	Project team composition due 5/29	Independence, Coorelation, Orthogonality Correlation vs Covariance; The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore;
3	6/1 - 6/5	Info Theory (Notes, L4) Optimization (Notes, L5) *Toolbox - P1/P2 (L6)		Q3 - L4, 5		Debugging Common Errors in NumPy; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
4	6/8 - 6/12	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/12 A2 out 6/12	Q4 - L7		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
5	6/15 - 6/19	GMM - Part 2 (Notes L8) DBSCAN (Notes L9)		Q5 - L8 \| Q6 - L9	Project proposal due 6/19 Peer Evaluation 6/24	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/22 - 6/26	Dimension Reduction (Notes L11) Linear Regression (Notes L12)		Q7 - L11		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
7	6/29 - 7/3	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 7/3 A3 out 7/3	Q8 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	7/6 - 7/10	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (*Notes L14)		Q9 - L13, 14	Project midpoint report 7/10 ( Note: You may submit it without penalty until 7/13. ) Peer Evaluation 7/17
9	7/13 - 7/17	Neural Networks (Notes L15) CNN (Notes L16)	A3 due 7/17 A4 out 7/17	Q10 - L15, 16		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction; 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;
10	7/20 - 7/24	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q11 - L17 \| Q12 - L18
11	7/27 - 7/28	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/27 can submit till 7/31 without any penalty		Final Project due 7/28 Peer Evaluation due 7/28	KKT and SVM Final Class days (July 28 and 29)