2020-21 TJ Course Guide Addendum

Machine Learning 1 (3199T1 – formerly Parallel Computing 1)

Credit: 0.5 credit (semester class) Additional Weight: 1.0 (AP weight)

Description: Machine Learning 1 at TJHSST is a 1‐semester course covering modern machine learning algorithms for classification, regression, analysis, evaluation and generation. The course covers classical techniques such as logistic and linear regression, support vector machines, decision trees, random forest, boosting, gradient descent and principal component analysis. Along the way best practice techniques for data processing, validation, model selection and accuracy evaluation are stressed.

Prerequisites: Artificial Intelligence 1 & 2

Pre- or Co-requisite: Multi‐Variable Calculus (may be taken before, or may be taken at the same time)  

Machine Learning 2 (3199T2 – formerly Parallel Computing 2)

Credit: 0.5 credit (semester class) Additional Weight: 1.0 (AP weight)

Description: Machine Learning 2 at TJHSST is a 1‐semester course covering modern neural network architectures and applications. This course topics include neural networks including dense neural networks, recurrent neural networks, convolutional networks, long‐short‐term‐memory, auto encoders and reinforcement learning. Along the way practice techniques for data processing, validation, model selection and accuracy evaluation are stressed.

Prerequisites: Machine Learning 1

Research Statistics 3 (3199T0 – formerly Cryptography)

Credit: 0.5 credit (semester class) Additional Weight: 0.5 (honors weight)

Description: Research Statistics 3 (RS3) at TJHSST is a 1‐semester course that is a thorough examination of Statistics from a non‐calculus‐based perspective. It will include a review of topics found in the AP Statistics curriculum, and optional topics such as Bayes’ Theorem, Simpson’s Paradox, and calculating the power of a test. The course will go beyond the AP curriculum to include ANOVA, multiple regression, logistic regression, and non‐parametric tests. The practice of statistics today requires computer software, students will create graphical displays, analyze data, and run tests using appropriate software.

Prerequisites: Research Statistics 1 & Research Statistics 2

Advanced Placement BC Calculus after Calculus AB (317706)

Credit: 1.0 credit (yearlong class)  Additional Weight: 1.0 (AP weight)

Description: This course provides a deeper understanding of the concepts of limits, continuity, derivatives, and integrals which were covered in AP Calculus AB. The major new topics covered are parametric, polar, and vector functions; Euler’s method; more L’Hopital’s Rule; special integration techniques; improper integrals; logistic differentiable equations; polynomial approximations and series; and Taylor Series.  This course emphasizes a multi-representational approach to calculus. Concepts, results, and problems are expressed graphically, numerically, analytically, and verbally. Graphing utilities and other relevant technology tools will be used when appropriate to support instruction, especially to allow students to explore graphical, numerical, and symbolic relationships. Content of this college-level course corresponds to the syllabus of the College Board Calculus BC Advanced Placement Program. Students who complete this course are encouraged to take the associated Advanced Placement examination and may earn college credit if a qualifying score is achieved.

Prerequisites: AP Calculus AB

Geospatial Analysis (422067)

Credit: 1.0 credit (yearlong class) Additional Weight: 0.5 (honors weight)

Description: Geospatial technologies are playing an ever-more central role in decision-making regarding a variety of issues across several fields, including environmental science, city planning, agriculture, emergency response, and many others.  This course provides students with the skills and knowledge to make use of geospatial technologies such as geographic information systems (GIS), global positioning systems (GPS), and remote sensing.   In this class, students will become acquainted with the tools, techniques and theory of GIS.  They will also develop a solid understanding of the different kinds of data that can be used with GIS.  Students will then use these skills and knowledge to complete a community-based research project involving spatial reasoning and decision-making. In the process, students develop critical thinking, spatial reasoning, communication, and collaboration skills, while addressing a problem that is important to their community.

Prerequisites: None