Update README.md

README.md

@@ -65,23 +65,23 @@ The exercises and sample solutions can be found in the corresponding lecture fol
 | Feb. 25 |  [Linear algebra and equations](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_03_Linear_algebra_and_equations) |  | Chapter 3. LU, QR, and Cholesky decomposition, condition numbers, Gauss-Jacobi and Gauss-Seidel methods. |
 | Feb. 27 | [Unconstrained optimization](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_04_Unconstrained_optimization) | [Video](Lecture_04_Unconstrained_optimization/video.md)  | Chapters 4 and 5. Search methods, bisection, gradient descent, Newton’s method, derivative free optimization (DFO). Applications to consumer demand and life-cycle problems, and maximum likelihood estimation. |
 | March 3 | [Nonlinear equations](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_05_Nonlinear_equations) | [Video](Lecture_05_Nonlinear_equations/video.md) | Chapters 4 and 5. Bisection, Newton’s method, BFGS and DFP updates, and Powell hybrid. Applications to general equilibrium and Nash equilibrium. |
-| March 5 | [Constrained optimization: theory and methods](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_06_Constrained_optimization_theory_and_methods/README.md) |  [Video](https://zoom.us/rec/play/6ZMrdrj8qWo3H4GStQSDBad-W467L_6sgXMW86YJzRu1VnlRMwGiNecXYeDNSQMTKq5xwl_XEBuHhnMO?startTime=1583425526000) | Chapters 4 and 5. Linear and nonlinear optimization. KKT conditions, augmented lagrangian, SQP and interior point methods. |
-| March 10 | [Constrained optimization: applications](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_07_Constrained_optimization_applications) |[Video](https://stanford.zoom.us/rec/share/v5B2LICrrEVJR42d00uAZb86HKn9eaa81HAd_fpbyUbknp-v6OpdziL3ADnoR2p4?startTime=1583857825000) | Introduction to multiobjective optimization. Applications to consumer demand and incentive problems |
+| March 5 | [Constrained optimization: theory and methods](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_06_Constrained_optimization_theory_and_methods/README.md) |  [Video](https://vimeo.com/396264921) | Chapters 4 and 5. Linear and nonlinear optimization. KKT conditions, augmented lagrangian, SQP and interior point methods. |
+| March 10 | [Constrained optimization: applications](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_07_Constrained_optimization_applications) |[Video](https://vimeo.com/398209165) | Introduction to multiobjective optimization. Applications to consumer demand and incentive problems |
 | March 12 | [Structural estimation I](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_08_Structural_estimation_I) | [Video](https://stanford.zoom.us/rec/share/ppNHN4DN9zlOAdaXyk7DV6EKEanAeaa8hiEY8_oLyxs2MTFi57I5Yda5ez5j7ozb?startTime=1584030961000) | Basic ideas. MPEC versus NFXP |
 | March 17 | [Finite-difference ODEs](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_09_Finite-difference_ODEs) | [Video](https://stanford.zoom.us/rec/play/uZB-Iryupjk3GtWTsgSDAPN-W43sfa-s23Ue-aFbnh6yVyYCZ1b3YeNHZeRsrOgCqalB98Ex_nGQBVGo?autoplay=true&startTime=1584548699000) |
 | March 19 | [Version control using Git](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_10_Version_control_using_Git) | [Video](https://vimeo.com/400185839) | *(Robert Erbe and Gregor Reich)* |
 | March 24 | [Automatic Differentiation](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_11_Automatic_Differentiation) |[Video](https://vimeo.com/400690807)| *(Philipp Mueller)* |
 | March 24 | [Automatic Differentiation - Tutorial](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_11_Automatic_Differentiation) || [CasADi Tutorial](http://live.casadi.org/) |
 | March 26 | [Homotopy](https://vimeo.com/402279793/70f5ba5c24) |[Video](https://vimeo.com/402279793) | *(Philipp Mueller and Karl Schmedders)* Chapter 5. Applications will include general equilibrium. |
-| March 30 | [Approximation I](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_13_Approximation_I) | [Video](https://stanford.zoom.us/rec/share/wPUyMvbfymJLTIXs40PdWfccEIrbaaa82ylL__tZyRvovvFH1pKy0GWaiUgsL86K?startTime=1585667158000) | Chapter 6. Interpolation, regression, orthogonal polynomials, splines, least squares, LAD and Lasso fits. |
+| March 30 | [Approximation I](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_13_Approximation_I) | [Video](https://vimeo.com/435446052/ba46c9b0a3) | Chapter 6. Interpolation, regression, orthogonal polynomials, splines, least squares, LAD and Lasso fits. |
 | April 2 | [Numerical quadrature, MC, qMC](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_14_Numerical_quadrature_MC_qMC) | [Video](https://stanford.zoom.us/rec/share/2M02cLbWyHhLZoWc2G7-e_YKQYb4aaa8gHJKq_FczEpocjf_LCT5qOSL4qwyOEaY?startTime=1585842361000) | Chapter 7, 8, and 9. Integration methods for single- and multiple-dimensional integrals. Monte Carlo simulation methods. Applications to portfolio choice and dynamic problems. |
 | April 7 | [Dynamic optimization, equilibrium, NLCEQ](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_15_Dynamic_optimization_equilibrium_NLCEQ) | [Video](https://stanford.zoom.us/rec/share/x-xzfurh5kNIT5HSq2CYeYNwDpzOaaa8h3NN8voPzEaCvLLgSy1OqATNa_fRRXQC?startTime=1586272903000) | |
 | April 9, 14, and 16 | No lectures. Easter break. | | |
-| April 21 | [Dynamic programming - discrete state](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_16_Dynamic_programming-discrete_state) | [Video](https://stanford.zoom.us/rec/share/yulPMpPprlFJWavxtHrieJIzIZ7uX6a8g3dMqPdbxBkj2bI5d0vDanHXuePdWysQ?startTime=1587482814000)| Chapter 12. Value function iteration, policy iteration, acceleration methods. |
-| April 23 | [Structural estimation II](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_17_Structural_estimation_II) | [Video](https://stanford.zoom.us/rec/share/25JSKOvO0l9LZLfn13HEGe07BaXgeaa80yEbqKZfnkt1cB7Vynqur-GGKQ6uPNHj?startTime=1587655522000)| Su-Judd and Skrainka-Judd papers. |
-| April 28 | [Dynamic programming - continuous state](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_18_Dynamic_programming-continuous_state) | [Video](https://stanford.zoom.us/rec/share/td12EpHS_WZLGIngxmiYHekEQN3vX6a81iMer_YKzRm_HKbRKCQ1l1xByg2xwjbd?startTime=1588087245000) | Chapter 12. Solutions to deterministic and stochastic dynamic programming problems using approximation, integration, and optimization methods. Applications to savings-consumption problems, climate change policy, and portfolio problems.|
-| April 30 | [Projection methods I](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_19_Projection_methods_I) |[Video](https://stanford.zoom.us/rec/share/4u8tdKP600xJRo3LzV7OavE5E9jpaaa8h3Iaq_oNzhshC-8T-Iq7gFA7HV6wvPib?startTime=1588261385000) | Chapter 10, 11, and 17. Methods for solving ordinary differential equations as well as the more complex equations arising in dynamic economic models. |
-| May 5 | [Projection methods II](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_20_Projection_methods_II) | [Video](https://stanford.zoom.us/rec/share/uYtZNe_RqkJLeafRxR_CSKtxL9_cX6a81iMcrPIKyUby52mzWqY4FaYD5jIW810W?startTime=1588692388000) | Chapter 10, 11, and 17. Methods for solving ordinary differential equations as well as the more complex equations arising in dynamic economic models. |
+| April 21 | [Dynamic programming - discrete state](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_16_Dynamic_programming-discrete_state) | [Video](https://vimeo.com/435508655/b5d9987c1a)| Chapter 12. Value function iteration, policy iteration, acceleration methods. |
+| April 23 | [Structural estimation II](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_17_Structural_estimation_II) | [Video](https://vimeo.com/435508636/3a39ff4457)| Su-Judd and Skrainka-Judd papers. |
+| April 28 | [Dynamic programming - continuous state](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_18_Dynamic_programming-continuous_state) | [Video](https://vimeo.com/435508680/a5e21053b7) | Chapter 12. Solutions to deterministic and stochastic dynamic programming problems using approximation, integration, and optimization methods. Applications to savings-consumption problems, climate change policy, and portfolio problems.|
+| April 30 | [Projection methods I](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_19_Projection_methods_I) |[Video](https://vimeo.com/435446109/7e20863187) | Chapter 10, 11, and 17. Methods for solving ordinary differential equations as well as the more complex equations arising in dynamic economic models. |
+| May 5 | [Projection methods II](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_20_Projection_methods_II) | [Video](https://vimeo.com/435446111/99a0cb4505) | Chapter 10, 11, and 17. Methods for solving ordinary differential equations as well as the more complex equations arising in dynamic economic models. |
 | May 7 | [Perturbation methods](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_21_Perturbation_methods) | [Video](https://stanford.zoom.us/rec/share/_8JYc-_5x21JY9LU6myCWY05Ip_Xeaa80SUa__UNyhlXcJxZpmUHXzaBBmdfMaFi?startTime=1588866315000) | Chapter 13, 14, and 15. Taylor series approximations to find numerical solutions of equations, linearizing around a steady state, simple bifurcation methods. |
 | May 12 | [Approximation II](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_22_Approximation_II) | [Video](https://stanford.zoom.us/rec/play/usIsIbqqrGg3GoWXswSDA6J5W9XseP-s23JLqPtYyk-0AnUFNVSiMrcXN7ctj5N3mBvDVHolwyqEvZqe?startTime=1589296129000&_x_zm_rtaid=vk-DMl74QcuOfOTtxVo9HA.1589456998615.0553ce3954bc4eaf4588d4724022529e&_x_zm_rhtaid=802) | Neural nets, radial basis functions, machine learning |
 | May 14 | [Dynamic games](https://github.com/KennethJudd/CompEcon2020/tree/master/Lecture_23_Dynamic_Games) |[Video](https://stanford.zoom.us/rec/share/zvdTCevx3FJLRI3i6Vr8fvQdJbrgT6a82iUe8qUOnkdxo7FYcI72mbxMOWfKgeU4?startTime=1589470895000) | |