Brief mention of interior point methods (Section 12.5 main textbook).Īssign f ourth homework project: Text feature extraction via LASSO and L1LR models Lecture 20: Algorithms for convex constrained optimization. Lecture 19: Quick survey of convex optimization algorithms. Lecture 18: More discrete models: Traveling salesman, ranking ordering models through binary integer optimization (this portion is in in Chapter 15 Langville-Meyer book and chapter 6 of Guenin et al.) Lecture 17-18: Discrete models: Shortest path problem, minimum cost perfect matching. Massey and Colley's methods (Chapters 1 to 3 of Langville-Meyer book). Lecture 15: Clustering models: K-means and first mention of discrete models for partitioning, packing problems, Knapsack models.Īssign t hird homework project: K-means for Cancer classification Lecture 13, 14: Discuss basics of unsupervised learning models PCA, graphical models. Lecture 12: More on semidefinite programming applications (Section 11.4 main textbook). Lecture 11: Semidefinite models (discuss matrix completion problem) (section 11.1 and 11.2 main textbook). Lecture 10: Basics of robust optimization, (sections 10.1, 10.3 main textbook). General supervised learning problem, kernel methods.Īssign s econd homework project: Breast cancer classification data. Lecture 8 and 9: Discuss basics of binary classification (Support vector machines) (Section 13.3 main textbook). Lecture 7: Discuss basics of supervised learning LASSO (section 13.2 main textbook). Lecture 5 and 6: Linear and quadratic optimization models (section 9.2-9.5 main textbook) Lecture 4: Duality: Lagrange dual, Karush-Kuhn Tucker (section 8.5 main textbook). Lecture 3: Optimality conditions of convex problems (section 8.4 main textbook). Lecture 2: Convex sets and convex functions, convex optimization (Section 8.1- 8.3 in main textbook). Quick review of MATLAB.įirst homework project is assigned: SVD and classification of digits. Singular value decomposition model for classification (briefly recall SVD if they do not remember it). What is data analytics? What is operations research? Discuss at least two examples of applications (e.g., optimal assignment marriage stable problem and medical school applicants).
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