List of Lecture Topics

  • Lecture 1- Optimization and Knapsack Problem
    • Computational models
    • Intro to optimization
    • 0/1 Knapsack Problem
    • Greedy solutions
  • Lecture 2 - Decision Trees and Dynamic Programming
    • Decision tree solution to knapsack
    • Dynamic programming and knapsack
    • Divide and conquer
  • Lecture 3 - Graphs
    • Graph problems
    • Shortest path
    • Depth first search
    • Breadth first search
  • Lecture 4 - Plotting
    • Visualizing Results
    • Overlapping Displays
    • Adding More Documentation
    • Changing Data Display
    • An Example
  • Lecture 5 - Stochastic Thinking
    • Rolling a Die
    • Random walks
  • Lecture 6 - Random Walks
    • Drunk walk
    • Biased random walks
    • Treacherous fields
  • Lecture 7 - Inferential Statistics
    • Probabilities
    • Confidence intervals
  • Lecture 8 - Monte Carlo Simulation
  • Lecture 9 - Monte Carlo Simulations
    • Sampling
    • Standard error
  • Lecture 10 - Experimental Data
    • Errors in Experimental Observations
    • Curve Fitting
  • Lecture 11 - Experimental Data
    • Goodness of Fit
    • Using a Model for Predictions
  • Lecture 12 - Machine Learning
    • Feature Vectors
    • Distance Metrics
    • Clustering
  • Lecture 13 - Statistical Fallacies
    • Misusing Statistics
    • Garbage In Garbage Out
    • Data Enhancement