Schedule #

Important

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

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

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	1/9 - 1/13	Course Overview (L1) Data analysis toolbox - P1 (L2)		Q0 - L1&2		GT Honor Code; 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	1/16 - 1/20	Linear Algebra (Notes, L3) Prob and Stats (Notes, L4)		Q1 - L3 & Syllabus Quiz		Correlation vs Covariance; Linear Algebra Review by Zico Kolter;
3	1/23 - 1/27	Prob and Stats - contd (Notes, L5) Info Theory (Notes, L6)	A1 out 1/23	Q2 - L4, L5,6		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	1/30 - 2/3	Optimization (L7) Toolbox - P2 (L8)		Q3 - L7,8	Project team composition due 2/3	KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
5	2/6 - 2/10	Clustering & K-Means (Notes L9) GMM - Part 1 (Notes L10)	A1 due 2/10 A2 out 2/10	Q4 - L9		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN);
6	2/13 - 2/17	GMM - Part 2 (Notes L11) Hierarchical Clustering (Notes L12)		Q5 - L10,11,12		Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
7	2/20 - 2/24	DB SCAN (Notes L13) Clustering Eval (Notes L14)		Q6 - L13,14	Project proposal due 2/24 Peer Evaluation 2/24	Jupyter Notbook (Kmeans and DBSCAN);
8	2/27 - 3/3	Density Estimation (Notes L15) Dimension Reduction (Notes L16)		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	3/6 - 3/10	Linear Regression (Notes L17) LR contd (Notes L18)	A2 due 3/10 A3 out 3/10	Q8 - L16, L17, L18		Simple Linear Regression in Matrix Format;
10	3/13 - 3/17	Regularization (Notes L19) NB & Logistic Reg (Notes L20)		Q9 - L19		Adding Noise to Regression Predictors;
11	3/20 - 3/24	Spring Break
12	3/27 - 3/31	NB & Logistic Reg (Notes L21) Project Practical Advice (Notes L22)		Q10 - L20,L21	Project midpoint report 3/31 AOE Peer Evaluation 3/31 AOE
13	4/3 - 4/7	Neural Networks (Notes L23) NN contd (Notes L24)	A3 due 4/7 A4 out 4/7	Q11 - L23,24		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction;
14	4/10 - 4/14	CNN (Notes L25) DT and RF (Notes DT & 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	4/17 - 4/21	SVM - Part 1 (Notes, L27) SVM - Part 2 (Notes, L28)	A4 Due 4/21	Q13 - L27,28
16	4/24 - 4/25	SVM-Kernel (Notes, L29) *Ethics in ML (L29) Final Class days (Apr 24 and 25)			Final Project due 4/25 Peer Evaluation due 4/25	KKT and SVM