1). Python Programming for AI Instructor Ming Zhang, Google Senior Software Engineer

Date: 5/19/2018 - 7/7/2018 Python has been used in many technical fields, especially for AI programming. This course will introduce the learner to the basics and some advanced features of the python programming, and prepare students for the AI programming.

This course will also guide the students to practice the programming of gRPC and protocol buffers. The gRPC is a modern, open source remote procedure call (RPC) framework that can run anywhere. It enables client and server applications to communicate transparently, and makes it easier to build connected systems.

The gRPC can use protocol buffers as both its IDL(interface definition language) and as its underlying message interchange format. Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler.

The gRPC and Protocol buffers are Google’s core technologies for a long time. It’s open to public in recent years. The current implementation is being used in several of Google’s cloud products and Google externally facing APIs. Through this course, students will learn basic hands-on Python skills, be able to solve practical client-server programming with Python, and be ready for AI projects with Python.

2). AI Applications with TensorFlow

Date: 7/14/2018 - 9/1/2018 This course will teach the fundamentals and contemporary usage of the Tensorflow library for deep learning capstone projects. The goal is to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. The main content of the course includes the following parts:

  • TensorFlow basics
  • Linear and Logistic Regression and TensorFlow Serving
  • DNN (Deep Neural Network), regularization, dropout, hyper-parameter tuning
  • CNN (Convolutional neural network)
  • RNN (Recurrent Neural Networks), LSTM and Seq2seq
  • Reinformance Learning

Through the teaching, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this teaching, we will be using Python.

About California Science and Technology University
California Science and Technology University (CSTU) is an academic institution of postgraduate learning that is located in Milpitas, and committed to provide a quality education to individuals whose goals include the development of rational, systematic, and critical thinking while striving to succeed in their chosen profession. CSTU was founded in 2011 and has been approved for operation by the government of California.

3). Project Management

Date: 9/8/2018 – 10/27/2018

Course Description This course introduces basic concepts, processes, and practices of project management and will be more specific on planning and managing projects in the Information Technology (IT) area. Project management concepts, methodologies, and tools will be explained with real-world examples and cases within the standard Project Management Institute (PMI) framework. Students will learn the skills necessary to define project scope, create workable project plans, and manage projects with quality, budget, and schedule in mind. Typical project management methods, such as Waterfall and Agile, and organization structures will be explained and compared. The course is structured around the key phases of project knowledge areas in mind, ranging from project scope, integration, stakeholder, to communication. In addition, students will be taught critical thinking on identifying and prioritizing potential issues and best practices in industry.

Course Objectives In this course, you will be able to:

  • tell key concepts, processes, and knowledge areas in project management;
  • communicate in standardized PMI project management languages with stakeholders;
  • plan and execute projects efficiently under the constraints;
  • identify issues and select solutions;
  • use project management tools and applications;
  • document project reports;
  • be able to work toward Project Management certificate.

Course Pre-requisites No pre-requisites. But any of the following knowledge will be beneficial:

  • A programming language;
  • Database administration;
  • Project management tools.