You will know about what data science is and how to do data science and what is required to do data science practically and also learn about Data Science, Data Sourcing, Basic coding, mathematics and statistics as well.

Table of Contents:

Part 1: Data Science: An Introduction: Foundations of Data Science
Welcome – Data Science: An Introduction – 1.1 
Demand for Data Science – Data Science: An Introduction – 2.1 
The Data Science Venn Diagram – Data Science: An Introduction – 2.2
The Data Science Pathway – Data Science: An Introduction – 2.3 
Roles in Data Science – Data Science: An Introduction – 2.4 
Teams in Data Science – Data Science: An Introduction – 2.5
Big Data – Data Science: An Introduction – 3.1 
Coding – Data Science: An Introduction – 3.2 
Statistics – Data Science: An Introduction – 3.3 
Business Intelligence – Data Science: An Introduction – 3.4 
Do No Harm – Data Science: An Introduction – 4.1 
Methods Overview – Data Science: An Introduction – 5.1 
Sourcing Overview – Data Science: An Introduction – 5.2 
Coding Overview – Data Science: An Introduction – 5.3 
Math Overview – Data Science: An Introduction – 5.4
Statistics Overview – Data Science: An Introduction – 5.5 
Machine Learning Overview – Data Science: An Introduction – 5.6
Interpretability – Data Science: An Introduction – 6.1 (1:4:00)
Actionable Insights – Data Science: An Introduction – 6.2
Presentation Graphics – Data Science: An Introduction – 6.3
Reproducible Research – Data Science: An Introduction – 6.4
Next Steps – Data Science: An Introduction – 7.1 (1:36:00)

Part 2: Data Sourcing: Foundations of Data Science ( 1:44:00)
Welcome – Data Sourcing – 1.1
Metrics – Data Sourcing – 2.1
Accuracy – Data Sourcing – 2.2
Social Context of Measurement – Data Sourcing – 2.3
Existing Data – Data Sourcing – 3.1
APIs – Data Sourcing – 3.2
Scraping – Data Sourcing – 3.3
New Data – Data Sourcing – 4.1
Interviews – Data Sourcing – 4.2
Surveys – Data Sourcing – 4.3
Card Sorting – Data Sourcing – 4.4
Lab Experiments – Data Sourcing – 4.5
A/B Testing – Data Sourcing – 4.6
Next Steps – Data Sourcing – 5.1

Part 3: Coding ( 2:36:00)
Welcome – Coding – 1.1
Spreadsheets – Coding – 2.1
Tableau Public – Coding – 2.2
SPSS – Coding – 2.3
JASP – Coding – 2.4
Other Software – Coding – 2.5
HTML – Coding – 3.1
XML – Coding – 3.2
JSON – Coding – 3.3
R – Coding – 4.1
Python – Coding – 4.2
SQL – Coding – 4.3
C, C++, & Java – Coding – 4.4
Bash – Coding – 4.5
Regex – Coding – 5.1
Next Steps – Coding – 6.1

Part 4: Mathematics (4:05:00)
Welcome – Mathematics – 1.1
Elementary Algebra – Mathematics – 2.1
Linear Algebra – Mathematics – 2.2
Systems of Linear Equations – Mathematics – 2.3
Calculus – Mathematics – 2.4
Calculus & Optimization – Mathematics – 2.5
Big O – Mathematics – 3.1
Probability – Mathematics – 3.2
Bayes’ Theorem – Mathematics – 3.3
Next Steps – Mathematics – 4.1

Part 5: Statistics (5:00:00)
Welcome – Statistics – 1.1
Exploration Overview – Statistics – 2.1
Exploratory Graphics – Statistics – 2.2
Exploratory Statistics – Statistics – 2.3
Descriptive Statistics – Statistics – 2.4
Inferential Statistics – Statistics – 3.1
Hypothesis Testing – Statistics – 3.2
Estimation – Statistics – 3.3
Estimators – Statistics – 4.1
Measures of Fit – Statistics – 4.2
Feature Selection – Statistics – 4.3
Problems in Modeling – Statistics – 4.4
Model Validation – Statistics – 4.5
DIY – Statistics – 4.6
Next Step – Statistics – 5.1

Source: https://datasciencedata.com/

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In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.

Instructors:

Percy Liang

Dorsa Sadigh

Lecture 1: Overview | Stanford CS221: AI (Autumn 2019)
Topics: Overview of course, Optimization
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 2: Machine Learning 1 – Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)
Topics: Linear classification, Loss minimization, Stochastic gradient descent
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 3: Machine Learning 2 – Features, Neural Networks | Stanford CS221: AI (Autumn 2019)
Topics: Features and non-linearity, Neural networks, nearest neighbors
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 4: Machine Learning 3 – Generalization, K-means | Stanford CS221: AI (Autumn 2019)
Topics: Generalization, Unsupervised learning, K-means
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 5: Search 1 – Dynamic Programming, Uniform Cost Search | Stanford CS221: AI (Autumn 2019)
Topics: Problem-solving as finding paths in graphs, Tree search, Dynamic programming, uniform cost search
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 6: Search 2 – A* | Stanford CS221: AI (Autumn 2019)
Topics: Problem-solving as finding paths in graphs, A*, consistent heuristics, Relaxation
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 7: Markov Decision Processes – Value Iteration | Stanford CS221: AI (Autumn 2019)
Topics: MDP1, Search review, Project
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 9: Game Playing 1 – Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)
Topics: Minimax, expectimax, Evaluation functions, Alpha-beta pruning
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Lecture 10: Game Playing 2 – TD Learning, Game Theory | Stanford CS221: AI (Autumn 2019)
Topics: TD learning, Game theory
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University

Source: https://stanford-cs221.github.io/autumn2019/

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This course introduces students to the basic knowledge representation, problem-solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem-solving, and learning in intelligent-system engineering; and appreciate the role of problem-solving, vision, and language in understanding human intelligence from a computational perspective.

Who is Patrick H. Winston?

Patrick H. Winston is Ford Professor of Artificial Intelligence and Computer Science at the Massachusetts Institute of Technology. He has been with CSAIL and before that the MIT Artificial Intelligence Laboratory since 1967. He joined the faculty in 1970, and he was the Director of the Artificial Intelligence Laboratory from 1972 to 1997.

Professor Winston is particularly involved in the study of how vision, language, and motor faculties account for intelligence. He also works on applications of Artificial Intelligence that are enabled by learning, precedent-based reasoning, and common-sense problem-solving.

Professor Winston is chairman and co-founder of Ascent Technology, Inc., a company that produces sophisticated scheduling, resource allocation, and schedule recovery applications, enabled by AI technology, and in use throughout the world in major airports and the Department of Defense.

Professor Winston was a member of the Naval Research Advisory Committee (NRAC) (1985-1990, 1994-2000) for which he served as Chair from 1997 to 2000. During his service on NRAC, he chaired several studies, including a study of how the Navy can best exploit the next generation of computer resources and a study of technology for reduced manning. Professor Winston is also a past president of the American Association for Artificial Intelligence.

Professor Winston is working on major new research and educational efforts, the Human Intelligence Enterprise, which will bring together and focus research from several fields, including Computer Science, Systems Neuroscience, Cognitive Science, and Linguistics.

Artificial Intelligence Lectures – 01
Introduction and Scope

Artificial Intelligence Lectures – 02
Reasoning: Goal Trees and Problem Solving

Artificial Intelligence Lectures – 03
Reasoning: Goal Trees and Rule-Based Expert Systems

Artificial Intelligence Lectures – 04
Search: Depth-First, Hill Climbing, Beam

Artificial Intelligence Lectures – 05
Search: Optimal, Branch and Bound, A*

Artificial Intelligence Lectures – 06
Search: Games, Minimax, and Alpha-Beta

Artificial Intelligence Lectures – 07
Constraints: Interpreting Line Drawings

Artificial Intelligence Lectures – 08
Constraints: Search, Domain Reduction

Artificial Intelligence Lectures – 09
Constraints: Visual Object Recognition

Artificial Intelligence Lectures – 10
Introduction to Learning, Nearest Neighbors

Artificial Intelligence Lectures – 11
Learning: Identification Trees, Disorder

Artificial Intelligence Lectures – 12a
Neural Nets

Artificial Intelligence Lectures – 12b
Deep Neural Nets

Artificial Intelligence Lectures – 13
Learning: Genetic Algorithms

Artificial Intelligence Lectures – 14
Learning: Sparse Spaces, Phonology

Artificial Intelligence Lectures – 15
Learning: Near Misses, Felicity Conditions

Artificial Intelligence Lectures – 16
Learning: Support Vector Machines

Artificial Intelligence Lectures – 17
Learning: Boosting

Artificial Intelligence Lectures – 18
Representations: Classes, Trajectories, Transitions

Artificial Intelligence Lectures – 19
Architectures: GPS, SOAR, Subsumption, Society of Mind

Lecture 20, which focuses on the AI business, is not available in MIT Lecture Videos due to unknown reasons..

Artificial Intelligence Lectures – 21
Probabilistic Inference I

Artificial Intelligence Lectures – 22
Probabilistic Inference II

Artificial Intelligence Lectures – 23
Model Merging, Cross-Modal Coupling, Course Summary

Source: https://ocw.mit.edu/

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Keypoints;
Artificial Intelligence in 5 min
Future Of Artificial Intelligence
Artificial Intelligence Application 2021
Should we be afraid of Artificial Intelligence
What is Artificial Intelligence
Machine Learning Part 1
Linear Regression Analysis
Decision Tree
Machine Learning Part 2
KNN algorithm Using Python
Mathematics For Machine Learning
Deep Learning Tutorial
TensorFlow 2.0 Tutorial for Beginners
Top 10 Artificial Intelligence Technologies in 2021

Source: 

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Topics include Introduction: Overview and Historical Perspective, Turing test, Physical Symbol Systems and the scope of Symbolic AI, Agents; State Space Search: Depth First Search, Breadth-First Search, DFID; Heuristic Search: Best First Search, Hill Climbing, Beam Search, Tabu Search; Randomized Search: Simulated Annealing, Genetic Algorithms, Ant Colony Optimization; Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches, Beam Stack Search; Problem Decomposition: Goal Trees, AO*, Rule-Based Systems, Rete Net; Game Playing: Minimax Algorithm, Alpha-Beta Algorithm, SSS*; Planning and Constraint Satisfaction: Domains, Forward and Backward Search, Goal Stack Planning, Plan Space Planning, Graphplan, Constraint Propagation; Logic and Inferences: Propositional Logic, First Order Logic, Soundness and Completeness, Forward and Backward chaining.

Mod-01 Lec-01 Artificial Intelligence: Introduction

Mod-01 Lec-02 Introduction to AI

Mod-01 Lec-03 AI Introduction Philosophy

Mod-01 Lec-04 AI Introduction

Mod-01 Lec-05 Introduction Philosophy

Mod-01 Lec-06 State Space Search Intro

Mod-01 Lec-07 Search-DFS and BFS

Mod-01 Lec-8 Search DFID

Mod-01 Lec-9 Heuristic Search

Mod-01 Lec-10 Hill Climbing

Mod-01 Lec-11 Solution Space Search,Beam Search

Mod-01 Lec-12 TSP Greedy Methods

Mod-01 Lec-13 Tabu Search

Mod-01 Lec-14 Optimization I (Simulated Annealing)

Mod-01 Lec-15 Optimization II (Genetic Algorithms)

Mod-01 Lec-16 Population Based Methods for Optimization

Mod-01 Lec-17 Population Based Methods II

Mod-01 Lec-18 Branch and Bound,Dijkstra’s Algorithm

Mod-01 Lec-19 A* Algorithm

Mod-01 Lec-20 Admissibility of A*

Mod-01 Lec-21 A* Monotone Property,Iteractive Deeping A*

Mod-01 Lec-22 Recursive Best First Search,Sequence Allignment

Mod-01 Lec-23 Pruning the Open and Closed Lists

Mod-01 Lec-24 Problem Decomposition with goal Trees

Mod-01 Lec-25 AO * Algorithm

Mod-01 Lec-26 Game Playing

Mod-01 Lec-27 Game Playing Minimax Search

Mod-01 Lec-28 Game Playing AlphaBeta

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The post Artificial Intelligence by UDACITY appeared first on New World : Artificial Intelligence.

Artificial Intelligence is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations.

Source: Intellipaat

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In other word, Artificial intelligence is the branch of computer science concerned with making computers behave like humans.

The frightening, futurist portrayals of Artificial Intelligence that dominate films and novels, and shape the popular imagination, are fictional. In reality, AI is already changing our daily lives, almost entirely in ways that improve human health, safety, and productivity. Unlike in the movies, there is no race of superhuman robots on the horizon or probably even possible. And while the potential to abuse AI technologies must be acknowledged and addressed, their greater potential is, among other things, to make driving safer, help children learn, and extend and enhance people’s lives. In fact, beneficial AI applications in schools, homes, and hospitals are already growing at an accelerated pace. Major research universities devote departments to AI studies, and technology companies such as Apple, Facebook, Google, IBM, and Microsoft spend heavily to explore AI applications they regard as critical to their futures. Even Hollywood uses AI technologies to bring its dystopian AI fantasies to the screen. (ARTIFICIAL INTELLIGENCE AND LIFE IN 2030 – REPORT OF THE 2015 STUDY PANEL)

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science.

Udacity was born out of a Stanford University experiment in which Sebastian Thrun and Peter Norvig offered their “Introduction to Artificial Intelligence” course online to anyone, for free. Over 160,000 students in more than 190 countries enrolled and not much later, Udacity was born.

Udacity, a pioneer in online education, is building “University by Silicon Valley”, a new type of online university that:
– teaches the actual programming skills that industry employers need today;
– delivers credentials endorsed by employers, because they built them;
– provides education at a fraction of the cost and time of traditional schools.

With industry giants – Google, AT&T, Facebook, Salesforce, Cloudera, etc. – Udacity offers Nanodegree credentials, designed so professionals become Web Developers, Data Analysts, or Mobile Developers. Supported by Udacity communities of coaches and students, Udacity students learn programming and data science through a series of online courses and hand-on projects that help them practice and build a convincing portfolio.

Stanford professors Peter Norvig and Sebastian Thrun are offering their ever popular Introduction to AI for free on the internet.

https://www.udacity.com/course/intro-to-artificial-intelligence–cs271

Norvig and Thrun, leaders in artificial intelligence, are using automated systems to help them at every level of the massive endeavour, from deciding which questions to answer to grading final exams.

In this course, you’ll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

It is free and it takes approximately 4 months. You can enroll in this course on Udacity and start learning there at once..

https://www.udacity.com/course/intro-to-artificial-intelligence–cs271

All courses’ videos have been uploaded by Udacity on Youtube..

“Artificial Intelligence Course was made in 2011 by Peter Norvig and myself at Stanford University and became basically the first very large massive open online course 160,000 students at the time then sparked the development of many many other such courses. The course is comprised of many units covering all the bases in artificial intelligence. It’s modelled after the very successful book by Stuart Russel and Peter Norvig which is the number one book on artificial intelligence and I really hope you get to enjoy this class it’s the original class diving.”  Sebastian THRUN, Co-FOUNDER&CEO, UDACITY

Artificial Intelligence: A Modern Approach (AIMA) is a university textbook on artificial intelligence, written by Stuart J. Russell and Peter Norvig.

  
Welcome to the Artificial Intelligence class on Udacity. Sebastian Thrun and Peter Norvig are your teachers. You can take this class at your own pace. You need to match up the quizzes, the homework assignments, the midterm and the final exam to succeed in this course.

THE PURPOSE OF THIS CLASS :
1. TO TEACH YOU THE BASICS OF ARTIFICIAL INTELLIGENCE (so you’ll be able to talk to people in the field and understand the basic tools of the trade)
2. TO EXCITE YOU ABOUT THE FIELD

“I have been in the field of artificial intelligence for about years, and it’s been truly rewarding. So I want you to participate in the beauty and the excitement of AI so you can become a professional who gets the same reward and excitement out of this field as I do.” Peter Norvig

STRUCTURE OF THIS COURSE:
*VIDEOS
*QUIZZES (which will ask you about your ability to answer AI questions)
*ANSWER VIDEOS ( which we tell you what the right answer would have been for the quiz that you might have falsely or incorrectly answered before.)
*ASSIGNMENT (you get a homework assignment also in the form of quizzes)
*VIDEO EXAMS

QUIZ:
AI PROGRAM IS CALLED ?
a. WETWARE
b. FORMULA
c. INTELLIGENT AGENT

QUIZ: (answer)
AI PROGRAM IS CALLED
a. WETWARE
b. FORMULA
c. INTELLIGENT AGENT (CORRECT)

Intelligent agent gets to interact with an environment. The agent can perceive the state of the environment through its sensors, and it can affect its state through its actuators. The big question of artificial intelligence is the function that maps sensors to actuators. That is called the control policy for the agent. So all of this class will deal with how does an agent make decisions that it can carry out with its actuators based on past sensor data. Those decisions take place many, many times, and the loop of environment feedback to sensors, agent decision, actuator interaction with the environment and so on is called perception action cycle.

QUIZ:
AI HAS SUCCESSFULLY BEEN USED IN ?
a. FINANCE
b. ROBOTICS
c. GAMES
d. MEDICINE
e. THE WEB
f.  NONE OF THEM

QUIZ: (answer)
AI HAS SUCCESSFULLY BEEN USED IN
a. FINANCE (correct)
b. ROBOTICS  (correct)
c. GAMES  (correct)
d. MEDICINE  (correct)
e. THE WEB  (correct)
f.  NONE OF THEM

“Every time you try to write a piece of software, that makes your computer software smart, likely you will need artificial intelligence. In this course, Peter and I will teach you many of the basic tricks of the trade to make your software really smart.” Sebastian Thrun

Some basic terminology that is commonly used in artificial intelligence to distinguish different types of problems.

FULLY VERSUS PARTIALLY OBSERVABLE:

An environment is called fully observable if what your agent can sense at any point in time is completely sufficient to make the optimal decision. For example, in many card games, when all the cards are on the table, the momentary site of all those cards is really sufficient to make the optimal choice.

That is in contrast to some other environments where you need memory on the side of the agent to make the best possible decision. For example, in the game of poker, the cards aren’t openly on the table, and memorizing past moves will help you make a better decision.

To fully understand the difference, consider the interaction of an agent with the environment to its sensors and its actuators, and this interaction takes place over many cycles, often called the perception-action cycle.

For many environments, it’s convenient to assume that the environment has some sort of internal state. For example, in a card game where the cards are not openly on the table, the state might pertain to the cards in your hand. An environment is fully observable if the sensors can always see the entire state of the environment.

It’s partially observable if the sensors can only see a fraction of the state, yet memorizing past measurements gives us additional information of the state that is not readily observable right now.

So any game, for example, where past moves have information about what might be in a person’s hand, those games are partially observable, and they require different treatment. Very often agents that deal with partially observable environments need to acquire internal memory to understand what the state of the environment is.

DETERMINISTIC VERSUS STOCHASTIC:

Deterministic environment is one where your agent’s actions uniquely determine the outcome. So, for example, in chess, there’s really no randomness when you move a piece. The effect of moving a piece is completely predetermined, and no matter where I’m going to move the same piece, the outcome is the same. That we call deterministic.

Games with dice, for example, like backgammon, are stochastic. While you can still deterministically move your pieces, the outcome of an action also involves throwing of the dice, and you can’t predict those. There’s a certain amount of randomness involved for the outcome of dice, and therefore, we call this stochastic.

DISCRETE VERSUS CONTINUOUS:

A discrete environment is one where you have finitely many action choices, and finally many things you can sense. So, for example, in chess, again, there are finally many board positions, and finally many things you can do.

That is different from a continuous environment where the space of possible actions or things you could sense may be infinite. So, for example, if you throw darts, there’s infinitely many ways to angle the darts and to accelerate them.

BENIGN VERSUS ADVERSARIAL:

In benign environments, the environment might be random. It might be stochastic, but it has no objective on its own that would contradict the own objective.

So, for example, weather is benign. It might be random. It might affect the outcome of your actions. But it isn’t really out there to get you.

Contrast this with adversarial environments, such as many games, like chess, where your opponent is really out there to get you. It turns out it’s much harder to find good actions in adversarial environments where the opponent actively observes you and counteracts what you’re trying to achieve relative to benign environment, where the environment might merely be stochastic but isn’t really interested in making your life worse.

CHECKERS
FULLY OBSERVABLE/DETERMINISTIC/DISCRETE/ADVERSARIAL

POKER
PARTIALLY OBSERVABLE/STOCHASTIC/ADVERSARIAL

ROBOTIC CAR
PARTIALLY OBSERVABLE/STOCHASTIC/CONTINUOUS

Artificial Intelligence is the technique of uncertainty management in computer software. AI is the discipline that you apply when you want to know what to do when you don’t know what to do.

There’s many reasons why there might be uncertainty in a computer program. There could be a sensor limit. That is, your sensors are unable to tell me what exactly is the case outside the AI system. There could be adversaries who act in a way that makes it hard for you to understand what is the case. There could be stochastic environments. Every time you roll the dice in a dice game, the stochasticity of the dice will make it impossible for you to be absolutely certain of what’s the situation. There could be laziness. So, perhaps you can actually compute what the situation is, but you computer program is just too lazy to do it. Ignorance; many people are just ignorant of what’s going on. They could know it, but they just don’t care.  All of these things are cause for uncertainty.

AI is the discipline that deals with uncertainty and manages it in decision making.

One of the best successes of AI technology at Google has been the machine translation system.

In Unit 1;

Key Applications of Artificial Intelligence

Definition of an Intelligent Agent

4 Key Attributes for Artificial Intelligence (partial observability, stochasticity, continuous spaces, and adversarial natures)

Sources and management of uncertainty

Mathematical concept of rationality are touched any of these issues superficially but as this class goes on you’re going to dive into any of those and learn much more about what it takes to make a truly intelligent AI systems.

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science.

Unit 2, Topic 1, Introduction

Unit 2, Topic 2, Route Finding Question

Unit 2, Topic 3, Route Finding

Unit 2, Topic 4, Tree Search

Unit 2, Topic 5, Tree Search Answer

Unit 2, Topic 6, Graph Search

Unit 2, Topic 7, Graph Search Answer

Unit 2, Topic 8, Graph Search Answer

Unit 2, Topic 9, More Graph Search

Unit 2, Topic 10, Graph Search Answer

Unit 2, Topic 11, Graph Search Termination

Unit 2, Topic 12, Uniform Cost Search

Unit 2, Topic 13, Uniform Cost Search

Unit 2, Topic 14, Uniform Cost Search

Unit 2, Topic 15, Uniform Cost Search

Unit 2, Topic 16, Uniform Cost Termination

Unit 2, Topic 17, Uniform Cost Termination Answer

YOU CAN FIND THE OTHERS ON YOUTUBE

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Barbara Hecker, J.D., Ph.D.
Barbara is an entrepreneur with a passion for teaching. She’s been a Department Chair, Chief Academic Officer and professor of Computer Science and Software Engineering at various state and private colleges for over 20 years. She has also founded several successful Silicon Valley start-up companies. She has earned master’s degrees in Software Engineering, Information Systems, and Telecommunications. She has also earned a Juris Doctorate and a PhD in Computer Science.

In all of her academic roles, she strives to provide superior graduate education, research and innovation. Outside of academia, Barbara’s interests and hobbies are in programming and software development. She is an innovative app developer and designer. Like most Silicon Valley entrepreneurs, she hopes to someday develop that “killer app,” get rich and then retire. In the meantime, she enjoys learning and sharing her experiences with others.

Educational Background

Ph.D. Computer Science
JD – Juris Doctor
M. S. Software Engineering
M. S. Information Systems
M. S. Telecommunications
B. S. Business Administration, Minor in Psychology

This video provides an introduction to the ITU Artificial Intelligence class. It only goes over the syllabus and the topics to be covered in this class.

Artificial Intelligence – Lecture 1
This lecture goes over the definition of AI and what might be considered AI. It discusses the concept of the “Turning Test” and whether or not modern day AI applications would pass it. The lecture also covers an overview of what AI is by definition and how to tell if a solution is AI based or not.

Artificial Intelligence – Lecture 2
This lecture covers the concept of Agents and the different types of Intelligent Agents and how they work together. The lecture provides and overview on how to design an Agent based solution. It also covers the different types of Agent Based solution scenarios.

Artificial Intelligence – Lecture 3
This lecture covers the concept of problem solving in Artificial Intelligence. It proposes a methodology for how to approach the solving problem technique and discusses how to design a solution to an Agent or Search based problem resolution.

Artificial Intelligence – Lecture 4
This lecture covers the concept of searching in informed and uninformed formats. Only an overview of searching as a concept is presented. The textbook goes over the implementation of the different searching algorithms that will be used in problem solving using searching methods.

Artificial Intelligence – Lecture 5
This lecture provides an overview of the LISP and Scheme programming languages. The lecture goes over an overview of what functional programming languages are and how they are used to solve problems. Dr. Racket installation and usage is also demonstrated.

Artificial Intelligence – Lecture 6
This lecture overviews the concept of Genetic Algorithms and how they apply to Artificial Intelligence. It provides a framework for understanding how this type of problem solving can be applied to computer science problems.

Artificial Intelligence – Lecture 7
This lecture provides an overview of how Prolog can be used to solve Artificial Intelligent logic related problems. It provides and overview of how prolog works with knowledge bases and with list processing. It’s just an orientation to the Prolog concept and functionality.

Artificial Intelligence – Lecture 8
This video goes over the final exam for the weekend AI class at ITU. General YouTube viewers are not going to find it helpful. It is intended for ITU students in their preparation for the final exam. Assignments 4, 5, Midterm and CSLO Essay are also described in this video.

Artificial Intelligence – Lecture 9
This lecture covers the concept of Expert Systems. The different types of systems are explored along with some examples. The concept of knowledge-bases, inference engines, rules, facts, and inferences is explored. The lecture is an overview of the concepts related to Expert Systems in general.

Artificial Intelligence – Lecture 10
This lecture discusses the concept of Knowledge Acquisition and Discovery. It overviews the concept of Knowledge Engineering and how to create a knowledge base in terms of the logic and AI components. It also finishes the discussion on how Expert Systems and how they work. An overview of the problems with using knowledge and Truth Maintenance is also explored though many different examples. Some of my elaborations and examples may sound off target but they are demonstrating knowledge issues and the value of keeping knowledge databases and information up-to-date in the real world.

Artificial Intelligence – Lecture 11
This lecture covers an overview of Bayesian Networks and how they are used in Artificial Intelligence. The concept of probability and general statistics applied to AI and network construction is also discussed. The lecture provides a high level overview without getting into the technical details about how the Bayes Nets are constructed. It’s for general concept and overview purposes.

Artificial Intelligence – Lecture 12
This lecture overviews the concept of Game Playing. The overall concept of designing AI applications to simulate game playing and game concepts is explored.

Artificial Intelligence – Lecture 13
This lecture covers the concept of Artificial Neural Networks and the different varieties that are used with computer artificial intelligence applications and problem solving. It is just an overview of the concepts and what makes up the ANN concepts.

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