Workshop Outline: Knowledge, Reasoning, and Uncertiainty with AI

Course Structure

Two 5-hour days:  2 hours session, lunchbreak, 3 hours session; for each day.Northwestern Polytechnic University

 

Prerequisites:  We will alternate between presentations and programming exercises.  The programming exercises are not required, but require use of a laptop with Python 3 installed.  Experience in Python would be very helpful.  If you don’t have these skills, you could pair up with someone who does.We will also be using a number of concepts of probability, statistics, and linear algebra.

 

Register today!

Course Description and Instructors

Where is NPU?

 

Questions? Contact us pr@npu.edu or 510-592-9688 ext 50

 

 

 

Day 1 - May 12

Day 2- May 19

 

Introduction

What do we mean by intelligence? 

What do we mean by artificial intelligence?

Examples of AI

What are the primary concepts and approaches used in AI?

 

Problem Solving

Solving problems by searching

Informed search methods

Game Playing

 

Programming Example:  Problem Solving

What tools and languages are commonly used?

Python

Sudoku example in Python

 

Knowledge and Reasoning

What is knowledge?

First-order logic

Building a knowledge base

Inference in first-order logic

Logical reasoning systems

 

Programming Example:  Knowledge and Reasoning

Rule-based systems

 

Uncertain Knowledge and Reasoning

Making simple decisions

Making complex decisions

 

Programming Example:  Uncertainty and Reasoning

Bayesian inference

 

Data Science

Definitions

Examples

 

Guest Presentation by Jenny Cai, Data Scientist at Moxie

This talk will cover typical projects carried out by Data Scientists.

Machine Learning

Learning from observations

Gradient Descent

Learning in Neural and Belief Networks

Reinforcement Learning

 

Programming Example:  Learning from data

Programming in Python Notebooks

Scikit-Learn

Linear Regression

 

Deep Learning/Neural Networks

Definitions

Why are these the current hot topic?

First set of examples

Structure of Neural Networks

Second set of examples

 

Programming Example:  Deep Learning

TensorFlow

Programming linear regression in TensorFlow

Keras and other frameworks

Back-Propagation

 

Specialized Types of Neural Networks

CNN’s

RNN’s

 

Programming Example:  CNN’s and Images

Using TensorFlow for digit recognition

Using TensorFlow for image classification

 

Guest Presentation from Mr. Utkarsch Contractor, Head of AI and Data Science at Aisera

This talk will focus on language and text processing

 

Conclusions

Current applications of AI

Reading list for further investigation