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