lecture linear

Pick anything below and it plays in the bar at the foot of the window — and keeps playing while you go on browsing the directory.

1. 1▶A Second Course in Algorithms (Lecture 15: Introduction to Approximation Algorithms)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
2. 2▶A Second Course in Algorithms (Lecture 14: Online Bipartite Matching)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
3. 3▶A Second Course in Algorithms (Lecture 13: Online Scheduling and Online Steiner Tree)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
4. 4▶A Second Course in Algorithms (Lecture 12: Applications of Multiplicative Weights to Games and LPs)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
5. 5▶A Second Course in Algorithms (Lecture 11: Online Learning and the Multiplicative Weights Algorithm)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
6. 6▶A Second Course in Algorithms (Lecture 10: The Minimax Theorem & Algorithms for Linear Programming)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
7. 7▶A Second Course in Algorithms (Lecture 9: Linear Programming Duality --- Part 2)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
8. 8▶A Second Course in Algorirthms (Lecture 8: Linear Programming Duality --- Part 1)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
9. 9▶A Second Course in Algorithms (Lecture 7: Linear Programming: Introduction and Applications)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
   +Watch later
10. 10▶A Second Course in Algorithms (Lecture 6: Generalizations of Maximum Flow and Bipartite Matching)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
11. 11▶A Second Course in Algorithms (Lecture 5: Minimum-Cost Bipartite Matching)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
12. 12▶A Second Course in Algorithms (Lecture 4: Applications of Maximum Flows and Minimum Cuts)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
13. 13▶A Second Course in Algorithms (Lecture 3: The Push-Relabel Algorithm for Maximum Flow)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
14. 14▶A Second Course in Algorithms (Lecture 2: Augmenting Path Algorithms for Maximum Flow)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
15. 15▶A Second Course in Algorithms (Lecture 1: Course Goals and Introduction to Maximum Flow)A Second Course in Algorithms (Stanford CS261, Winter 2016)Notes
    +Watch later
16. 16▶Lecture 01 - The Learning ProblemMachine Learning Course - CS 156Notes
    +Watch later
17. 17▶Lecture 15 - Kernel MethodsMachine Learning Course - CS 156Notes
    +Watch later
18. 18▶Lecture 14 - Support Vector MachinesMachine Learning Course - CS 156Notes
    +Watch later
19. 19▶Lecture 13 - ValidationMachine Learning Course - CS 156Notes
    +Watch later
20. 20▶Lecture 12 - RegularizationMachine Learning Course - CS 156Notes
    +Watch later
21. 21▶Lecture 11 - OverfittingMachine Learning Course - CS 156Notes
    +Watch later
22. 22▶Lecture 10 - Neural NetworksMachine Learning Course - CS 156Notes
    +Watch later
23. 23▶Lecture 09 - The Linear Model IIMachine Learning Course - CS 156Notes
    +Watch later
24. 24▶Lecture 08 - Bias-Variance TradeoffMachine Learning Course - CS 156Notes
    +Watch later
25. 25▶Lecture 07 - The VC DimensionMachine Learning Course - CS 156Notes
    +Watch later
26. 26▶Lecture 06 - Theory of GeneralizationMachine Learning Course - CS 156Notes
    +Watch later
27. 27▶Lecture 05 - Training Versus TestingMachine Learning Course - CS 156Notes
    +Watch later
28. 28▶Lecture 04 - Error and NoiseMachine Learning Course - CS 156Notes
    +Watch later
29. 29▶Lecture 03 -The Linear Model IMachine Learning Course - CS 156Notes
    +Watch later
30. 30▶Lecture 02 - Is Learning Feasible?Machine Learning Course - CS 156Notes
    +Watch later