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	5/12 - 5/16	Course Overview (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	5/19 - 5/23	Prob and Stats (Notes, L3) Info Theory (Notes, L4)	A1 out 5/19	Q2 - L3 \| Q3 - L4 \| Syllabus Quiz	Project team composition due 5/23	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	5/26 - 5/30	Memorial Day Holiday Optimization (L5) Toolbox - P1/P2 (L6)		Q4 - L5		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	6/2 - 6/6	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/6 A2 out 6/6	Q5 - L7 \| Q6 - L8		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
5	6/9 - 6/13	GMM - Part 2 (Notes L8) DBSCAN (Notes L9) Clustering Eval (Notes L10-A) Density Estimation (Notes L10-B)		Q7 - L9, L10-A (Density Estimaion NOT included)	Project proposal due 6/13 Peer Evaluation 6/18	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/16 - 6/20	Density Estimation - Contd (Notes L10-B) Dimension Reduction (Notes L11) Juneteenth Holiday		Q8 - L10-B (Density Estimaion included), L11		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
7	6/23 - 6/27	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 6/27 A3 out 6/27	Q9 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	6/30 - 7/4	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (*Notes L14)		Q10 - L13, 14	Project midpoint report 7/4 ( Note: You may submit it without penalty until 7/7. ) Peer Evaluation 7/11
9	7/7 - 7/11	Neural Networks (Notes L15) *CNN (Notes L16)	A3 due 7/11 A4 out 7/11	Q11 - 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	7/14 - 7/18	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q12 - L17 \| Q13 - L18
11	7/21 - 7/22	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/21 can submit till 7/25 without any penalty		Final Project due 7/22 Peer Evaluation due 7/22	KKT and SVM Final Class days (July 22 and 23)