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PharmD Program Prerequisites - School of Pharmacy

https://pharmacy.temple.edu/pharmdprereqs

Complete our Doctor of Pharmacy program prerequisites. We encourage you to identify the quickest and most economical ways to complete the following prerequisites at any accredited college or university, in person or online. If you want to make sure that you have found a course that meets the criteria for a Temple University course prerequisite below, please send an email to rxadmis@temple.edu ...

Pre-College Programs | Undergraduate Admissions

https://admissions.temple.edu/summer/pre-college

Two-week, Pre-College educational opportunity taking place in Rome, Italy.

Lecture Set 4 – Selection Structures - Temple University

https://cis.temple.edu/~friedman/cis071/Lecture4-web.doc

The book also uses flow charts, which are sometimes handy for visualizing what is going on with a decision structure. However, pseudo-code, if properly indented and written with a little care is a preferred mechanism for mapping out the logic of a decision before trying to program it (and having to worry about syntax).

A Faculty Guide to A.I. - Center for the Advancement of Teaching

https://teaching.temple.edu/teaching-technologies/faculty-guide-ai

The speed with which generative AI tools have been emerging and improving can leave us all feeling a bit lost and overwhelmed. While your inclination may be to hide under the proverbial covers and carry on as before, it’s important to intentionally approach possible use of AI tools by your students. Approaching AI proactively rather than reactively will save stress where students use the ...

University College Expands High-Impact Certificate Course Offerings ...

https://ambler.temple.edu/news/2025/06/university-college-expands-high-impact-certificate-course-offerings-through-partnership-ziplines-education

University College at Temple University, through its Office of Non-Credit and Continuing Education (ONCE), has partnered with Ziplines Education, a national career accelerator focused on upskilling professionals at all stages of their careers. Through this collaboration, ONCE is introducing a new portfolio of online certificate programs designed to provide students and professionals in the ...

Qualitative Data Analysis and QDA Tools - Temple University

https://guides.temple.edu/qda/qualcoder

QualCoder is free, open source software for qualitative data analysis. It has many of the features of commercial QDA software packages such as auto-coding, coding images and A/V materials, SQL database querying, and many reporting and visualization options.

Game Theory Introduction

https://sites.temple.edu/gametheory/2024/03/12/introduction/

Game theory, the mathematical study of strategy and decision-making among competing agents, has a rich and multifaceted history that intersects with economics, mathematics, psychology, and even biology. Its development has been influenced by numerous scholars across these fields, each contributing their own unique perspectives and insights. Origins and Early Development The formal inception of ...

Distributed System Design: An Overview* - Temple University

https://cis.temple.edu/~wu/teaching/Spring2018/distributed-computing-2018.pdf

1. In your opinion, what is the future of the computing and the field of distributed systems? 2. Use your own words to explain the differences between distributed systems, multiprocessors, and network systems. 3. Calculate (a) node degree, (b) diameter, (c) bisection width, and (d) the number of links for an nx n2-d mesh, an n x n2- d torus, and an n-dimensional hypercube.

TileSR: Accelerate On-Device Super-Resolution with Parallel Offloading ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf

TileSR: Accelerate On-Device Super-Resolution with Parallel Ofloading in Tile Granularity Ning Chen1, Sheng Zhang1∗, Yu Liang2, Jie Wu3, Yu Chen1, Yuting Yan1, Zhuzhong Qian1 and Sanglu Lu1

PowerPoint Presentation

https://cis.temple.edu/~latecki/Courses/AI-Fall11/Lectures/ch7EL.ppt

On each iteration t, we find a classifier h(x) that minimizes the error with respect to the distribution. Next we increase weights of training examples misclassified by h(x), and decrease weights of the examples correctly classified by h(x) The new distribution is used to train the next classifier, and the process is iterated.