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Introduction to Computer Science and Programming
Unit 1
Lecture 1: Introduction to 6.00 (41:27)
Lecture 2: Core Elements of a Program (102:16)
Lecture 3: Problem Solving (47:55)
Lecture 4: Machine Interpretation of a Program (107:58)
Lecture 5: Objects in Python (50:58)
Lesson 6: Recursion (99:45)
Lecture 7: Debugging (100:20)
Lecture 8: Efficiency and Order of Growth (92:20)
Lecture 9: Memory and Search Methods (48:07)
Quiz 1
Unit 2
Lecture 1: Hashing and Classes (44:56)
Lecture 2: OOP and Inheritance (103:13)
Lecture 3: Introduction to Simulation and Random Walks (100:46)
Lecture 4: Some Basic Probability and Plotting Data (42:48)
Lecture 5: Sampling and Monte Carlo Simulation (50:51)
Lecture 6: Statistical Thinking (51:30)
Lecture 7: Using Randomness to Solve Non-random Problems (49:43)
Lecture 8: Curve Fitting (98:25)
Lecture 9: Optimization Problems and Algorithms (49:41)
Lecture 10: More Optimization and Clustering (49:42)
Quiz 2 (124:44)
Unit 3
Lecture 1: More Clustering (99:56)
Lecture 2: Using Graphs to Model Problems, Part 1
Lecture 3: Using Graphs to Model Problems, Part 2 (98:50)
Lecture 4: Dynamic Programming (53:40)
Lecture 5: Avoiding Statistical Fallacies (38:29)
Lecture 6: Queuing Network Models (105:16)
Lecture 7: What Do Computer Scientists Do? (50:04)
Final Exam
Lecture 2: Using Graphs to Model Problems, Part 1
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