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	5/13 - 5/17	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	5/20 - 5/24	Prob and Stats (Notes, L3) Info Theory (Notes, L4)	A1 out 5/20	Q2 - L3 \| Q3 - L4 \| Syllabus Quiz	Project team composition due 5/24	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	5/27 - 5/31	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	6/3 - 6/7	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/7 A2 out 6/7	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	6/10 - 6/14	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 6/14 Peer Evaluation 6/19	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/17 - 6/21	Juneteenth Holiday Density Estimation - Contd (Notes L10-B) Dimension Reduction (Notes L11)		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	6/24 - 6/28	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 6/28 A3 out 6/28	Q9 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	7/1 - 7/5	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (*Notes L14)		Q10 - L13, 14	Project midpoint report 7/3 Peer Evaluation 7/10
9	7/8 - 7/12	Neural Networks (Notes L15) CNN (Notes L16)	A3 due 7/12 A4 out 7/12	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	7/15 - 7/19	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q12 - L17 \| Q13 - L18
11	7/22 - 7/23	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/23 can submit till 7/26 without any penalty		Final Project due 7/23 can submit till 7/26 without any penalty Peer Evaluation due 7/26	KKT and SVM Ethics in ML Final Class days (July 22 and 23)