* * * This page has been archived (May 2022). * * *

* * * This page has been archived (May 2022). * * *

Courses 2021-22

**Resources**

- Board Pictures: Computation & Modeling, 11th Grade Machine Learning (Algorithms, Space Empires), 12th Grade Machine Learning (Algorithms, Space Empires)
- Fall Midterms: 9/23 Machine Learning (11th & 12th), 11/12 Computation & Modeling (10th), 11/12 Machine Learning (11th & 12th)
- Fall Final: Machine Learning (11th & 12th), Computation & Modeling (10th)
- 2/25 Spring Midterms (all classes)

**Assignments**

Note: If an assignment does not have a link, then it was written on the board, and it can be found in the "Board Pictures" photo album in your respective grade level (on this page, above).

Computation & Modeling | Machine Learning (11th Grade) | Machine Learning (12th Grade) |

Continue set 31 | Evolving Neural Nets | Continue Blondie24 Project |

Set 31: Snake | Space Empires - combat phase | Continue Blondie24 Project |

Set 30: Connect Four Custom Strategy | Simplex Method - matrix operations and implementation | Continue Blondie24 Project |

Set 29: Connect Four | Space Empires | Part 3 of Blondie24 Project |

Set 28: Tic-Tac-Toe Competition | Close the loop on genetic algorithms, begin Simplex Method | Finish part 2 and begin part 3 of Blondie24 Project |

Set 27: Tic-Tac-Toe Game Tree | Space Empires | Blondie24 Project - parts 1 and 2 |

Set 26: Custom Tic-Tac-Toe Strategy (plus some other small exercises) | Space Empires | Finish GA 2.0 |

Set 25: Dijkstra's Algorithm | GA 2.0: Continued genetic algorithms on tic-tac-toe: tournament selection, tournament fitness, mutation rates | GA 2.0: Continued genetic algorithms on tic-tac-toe: tournament selection, tournament fitness, mutation rates |

Set 24: Tic-Tac-Toe with Random Players | GA 1.0: Intro to genetic algorithms on tic-tac-toe | GA 1.0: Intro to genetic algorithms on tic-tac-toe |

Set 23: Distance and Shortest Path in Graphs, Data Normalization | Space Empires | Space Empires |

Set 22: Cross-Validation with KNN, Exam Prep EXAM FRIDAY! Topics include multivariable gradient descent, fitting regressions via gradient descent on RSS, SQL (join, group by, subqueries), Euler estimation for systems of differential equations, k-means and elbow method, anything from last semester's final. | Beginning shared implementation of Space Empires EXAM FRIDAY! The main topic is neural networks - direct differentiation, backpropagation, good vs bad activation functions, etc. Plus anything from last semester's final. | Space Empires EXAM FRIDAY! Topics include PCA, fitting models to general data sets (mini kaggle), simplex method, Q-learning, anything from last semester's final. |

Set 21: Overfitting, Underfitting, Cross-Validation EXAM NEXT FRIDAY! (2/25) | Neural net regressor with multiple hidden layers EXAM NEXT FRIDAY! (2/25) | Intro to Q-learning EXAM NEXT FRIDAY! (2/25) |

Set 20: SQL, Haskell | Space Empires - begin shared implementation | Space Empires - testing random player on economic phase; introduce planets/asteroids |

Set 19: Elbow Method, SQL | Implement backpropagation algorithm in hard-coded neural net | Simplex method |

Set 18: K-Means, Self Joins, Haskell | Finish Space Empires competition, intro to backpropagation algorithm | Space Empires - combat / economic phase log & random strategy development for simulation testing |

Set 17: Generalizing Euler Estimator to Systems of Differential Equations | Space Empires - economic phase strategy development + competition | Mini kaggle competition (on a toy dataset generated by the instructor) |

Set 16: SQL, Logistic Regression via Gradient Descent, Euler Estimation | Regressing a linear combination of nonlinear functions via neural network | Space Empires |

Set 15: Linear and Polynomial Regression via Gradient Descent | Final exam corrections; Space Empires - economic phase | Final exam corrections; principal component analysis |

Set 14: SQL, Multivariable Gradient Descent | Benefits of gradient descent over transformations into linear space: power regression and exponential regression | Benefits of gradient descent over transformations into linear space: power regression and exponential regression |

Set 13: SQL, Gradient Descent, Operator Overloading, kNN | Space Empires Competition 2 | Space Empires |

Set 12: DataFrames from Scatch | Neural Nets: forward propagation by hand with simple activation f(x) = 2x, classification boundaries with arbitrary activation functions | Keras: https://www.tensorflow.org/tutorials/keras/classification (for each tutorial we cover, walk through it and be ready to discuss it the following day) |

• Set 11: Logistic Regression with General Bounds + Data Set Analysis using DataFrames • Exam Friday (11/12) - sorting algorithms, Newton-Raphson, recursive vs non-recursive matrix determinant, linear/logistic regression, breadth-first and depth-first traversals | • Space Empires - first competition • Exam Friday (11/12) - multivariable gradient descent, interpreting/predicting plots of running ML algorithms on data sets with particular features; random forests (11th only) and backpropagation algorithm (12th only) | • Neural net plots using sklearn implementations • Exam Friday (11/12) - multivariable gradient descent, interpreting/predicting plots of running ML algorithms on data sets with particular features; random forests (11th only) and backpropagation algorithm (12th only) |

• Set 10: Interaction Terms and Intro to DataFrames • Exam NEXT Friday (11/12) | • Space Empires • Exam NEXT Friday (11/12) | • Space Empires • Exam NEXT Friday (11/12) |

Set 9: Regression with Multiple Inputs, Breadth-First and Depth-First Traversals, Intro to DataFrames | Plots using artificial data set and sklearn implementations of kNN, decision tree, random forest | Plots using artificial data set and sklearn implementations of kNN, decision tree, random forest |

Set 8: Determinant via RREF, Merge Sort, Intro to Graphs | Space Empires - running standardized strategies on each other's games | Space Empires - visualization |

Set 7: Linear and logistic regressor classes | Implement random forests | Implement backpropagation |

Set 6: Matrix Inverse | Space Empires - standardize strategy class | Space Empires - get core game engine running |

Continue set 5 | Decision tree pruning | Implement bias nodes and learn about activation functions |

Set 5: RREF Algorithm | • Space Empires - write tests • Exam Thursday (9/23) - gradient descent, bias-variance tradeoff, decision trees (11th) and neural nets (12th) | • Space Empires - coordinate to get tests working • Exam Thursday (9/23) - gradient descent, bias-variance tradeoff, decision trees (11th) and neural nets (12th) |

Set 4: Generalized Matrix Arithmetic, Newton-Raphson, Merging | • Finish up set 2, and then continue Space Empires development - creates strategy players • Exam NEXT Thursday (9/23) - gradient descent, bias-variance tradeoff, decision trees | • Finish up set 2, and then continue Space Empires development - coordinate to write tests • Exam NEXT Thursday (9/23) - gradient descent, bias-variance tradeoff, neural nets |

Set 3: Basic Sorting and Matrices | Set 2: Decision Trees | Set 2: Intro to Neural Nets |

Set 2: Brute-Force Search, Bisection Search, Intro to Data Structures and Sorting | Space Empires development - refactor and extend game to multiple ship types | Space Empires development - begin rewriting in Javascript |

Set 1: Some Basic Problems | Set 1: Quant Review in Julia | Set 1: Quant Review in Julia |