In this recitation, the class wraps up with dynamic programming and specifically the technique of memoization. Many code examples are presented to show all the applications of this technique.

Recitation 10: Introduction to Dynamic Programming

This optional recitation reviews algorithm complexity and object-oriented programming in great detail with a focus on big O notation.

Optional Recitation: Algorithm Complexity and Class Review

In this review session the solution to practice quiz 2 are presented in detail, and the main topics of unit 2 (on the quiz 2 topics sheet) as well as some technical topics are explained with some code examples.

Quiz 2 Review Session

This recitation reviews the details of directed and undirected node graphs as well as explaining the difference between depth-first search and breath-first search with an extensive explanation of the code involved in graph creation and the search methods

Recitation 9: Directed and Undirected Node Graphs

This recitation reviews hierarchical and kmeans clustering in great detail with many different code examples. It also discusses the merits and disadvantages of both clustering and how to calculate error.

Recitation 8: Hierarchical and k-means Clustering

This recitation video covers some basic probability and statistics, as well as simulation methods for estimating unknown probabilites.  Includes worked examples, probability distributions, mean and standard deviation, and plotting graphs in Python.

Recitation 6: Probability and Statistics

This recitation discusses different types of data distributions, Monte Carlo simulations and data curve regressions in great detail and is heavy in code examples and explanations.

Recitation 7: Distributions, Monte Carlo, and Regressions

This recitation goes over the answers to Quiz 1 and introduces object-oriented programming (classes) in comparison to types.

Recitation 5: Quiz 1 Answers and Object-Oriented Programming

This recitation covers lists, list elements, and a discussion of sorting techniques.  Introduces recursion, base cases, and simplifying problems.

Recitation 3: Lists and their Elements, Sorting, and Recursion

This recitation further explains recursion, pseudo code and debugging with several examples. The recitation also talks about optional parameters and floating point comparisons and float's representation in python.

Recitation 4: Recursion, Pseudo code, and Debugging

OCW Scholar: Introduction to Computer Science and Programming

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I had seen this on opencourseware website before the release of iTunes u, and it was great. They teach the basic concepts all programmers should know for any language. They teach in python, but the application of knowledge is for any program. Highly recommend.

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This recitation reviews the basics and the specifics of loops, tuples, strings, and functions in detail.

Recitation 2: Loops, Tuples, Strings, and Functions

This recitation covers an introduction to what coding is, how the computer executes code, and to simple coding constructs, including syntax, semantics, object types, if statements, and loops.

Recitation 1: Introduction to Coding Concepts

This lecture covers dynamic programming, optimal path, overlapping subproblems, specifications, restrictions, efficiency, and pseudo-polynomials.

Lecture 23: Dynamic Programming

This lecture covers graphs and graphing techniques in Python.  Includes: pseudocode, nodes, edges, adjacency matrices, and adjacency lists.

Lecture 21: Using Graphs to Model Problems, Part 1

This lecture covers clustering, include feature vectors, scaling, and k-means clustering.

Lecture 20: More Clustering

In the final lecture we will discuss careers in computer science, computational thinking, abstraction, and automation.

Lecture 26: What Do Computer Scientists Do?

This lecture covers queuing networks and simulations, Poisson distributions, wait time, queue length, server utilization, FIFO, LIFO, and SRPT.

Lecture 25: Queuing Network Models

This lecture discusses common statistical fallacies and how to avoid them.  Topics include: statistics, plotting, correlation, causation, bias, logical fallacies, data enhancement, and the Texas sharpshooter fallacy.

Lecture 24: Avoiding Statistical Fallacies

This lecture continues discussion on analytical vs. simulation models for problem solving.  Topics include Gaussian distributions, analytical models, simulations, exponential growth, probability, distributions, Monty Hall problem

Lecture 16: Using Randomness to Solve Non-random Problems

In this lecture we continue working with simulations, covering the Monte Carlo simulation, Pascal's algorithm, inferential statistics, the gambler's fallacy, and the law of large numbers.

Lecture 14: Sampling and Monte Carlo Simulation

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