Introduction to Computational Thinking and Data Science
Lecture 1: Introduction, Optimization Problems
- Computer Models
- Optimization models
- Knapsack problem
- Brute force for optimization problems
- Greedy algorithm for optimization problems
Lecture 2: Optimization problems
Lecture 3: Graph-theoretic models
Lecture 4: Stochastic Thinking
- Uncertainty
- Stochastic processes
- Probability
- Random numbers
- Sample probability
- The Birthday Problem
- Simulation Models
Lecture 5: Random Walks
Lecture 6: Monte Carlo Simulation
- Monte Carlo Simulations
- Inferential Statistics
- Confidence intervals
- Law of Large Numbers
- Gambler’s Fallacy
- Regression to the Mean
- Variance
- Empirical Rule
- Probability Distributions
- Probability Density Function
- Normal Distributions
Lecture 7: Confidence Intervals
Lecture 8: Sampling and standard error
- Inferential Statistics
- Monte Carlo Simulations
- Sampling
- Confidence intervals
- Standard of the Error Mean
- Skew
Lecture 9: Understanding Experimental Data
- Data
- Modelling a spring
- Objective Functions
- Least Squares Objective Function
- Linear regression
- Coefficient of Determination
Lecture 10: Understanding Experimental Data (Cont.)
Lecture 11: Introduction to Machine Learning
- Linear regression
- Machine Learning
- Supervised-Unsupervised Learning
- Clustering
- Features
- Minkowski Metric (Distance)
- Model metrics
Lecture 12: Clustering
(Pending)
Lecture 13: Classification
(Pending)
Lecture 14: Classification and statistical sins
(Pending)
Lecture 15: Statistical Sins and Wrap Up
(Pending)