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Seattle Prerequisites to Learning AI [Apr 14 - May 6, 2018] Training | Artificial Intelligence | Machine Learning | Deep Learning | IT Training | Disruptive Technologies

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Endorsed by Curators:
Apr 14 8:00AM - 10:00AM

Video Conference Details

Will be sent after registration and payment

Course Overview

The prerequisites to learning Disruptive technologies include:

  • Probability
  • Statistics
  • Linear Algebra
  • Calculus (Differential, Multivariate)
  • Data Structures
  • Algorithms
  • High level Programming Language such as Python for beginners
  • Object Oriented Programming Language such as Java (Optional)

About this course

  • This course is structured according to the background and existing knowledge of the students.
  • The goal of this course is to learn the prerequisites quickly and move on to learn the disruptivetechnologies
  • The instructor(s) and the students together decide what they want to skip and what they wantto learn based on this comprehensive course outline mentioned below. Instructor can add, editthe course outline to suit the class.

What you will learn in this course?

In this course, youll learn the foundational knowledge which will be useful in learning disruptivetechnologies.

What are the pre-requisites?

  • No prerequisite is required.
  • Some statistics, probability, computer and programming background will be helpful

Comprehensive and Detailed Course Outline

Computer Programming for those with no programming background (if required, otherwise skiptonext section)

  • Intended for students without prior programming experience.
  • Basic programming-in-the-small abilities and concepts including procedural programming(methods, parameters, return, values)
  • Basic control structures (sequence, if/else, for loop, while loop)
  • File processing
  • Arrays
  • An introduction to defining objects.

Intermediate Computer Programming for those with some programming background

  • Concepts of data abstraction and encapsulation including stacks, queues, linked lists, binarytrees, recursion, instruction to complexity and use of predefined collection classes.

Data Structures & Algorithms

  • Fundamental algorithms and data structures for implementation
  • Techniques for solving problems by programming
  • Linked lists, stacks, queues, directed graphs.
  • Trees: representations, traversals.
  • Searching (hashing, binary search trees, multiway trees).
  • Garbage collection, memory management.
  • Internal and external sorting
  • Abstract data types and structures including dictionaries, balanced trees, hash tables, priority
  • queues, and graphs
  • Sorting; asymptotic analysis; fundamental graph algorithms including graph search, shortest
  • path, and minimum spanning trees; concurrency and synchronization;

Foundations of Computing

  • Examines fundamentals of logic
  • Set theory, induction
  • Algebraic structures with applications to computing
  • Finite state machines
  • Limits of computability

Probability & Statistics

  • Visualizing relationships in data
  • Seeing relationships in data.
  • Making predictions based on data.
  • Simpson's paradox.
  • Probability
  • Introduction to Probability.
  • Bayes Rule.
  • Correlation vs. Causation.
  • Estimation
  • Maximum Likelihood Estimation.
  • Mean, Median, Mode.
  • Standard Deviation and Variance.
  • Outliers and Normal Distribution.
  • Outliers, Quartiles.
  • Binomial Distribution.
  • Manipulating Normal Distribution.
  • Inference.
  • Confidence Intervals.
  • Hypothesis Testing.
  • Regression
  • Linear regression.
  • Correlation.

Linear algebra

  • Vectors
  • Vectors and spaces
  • Matrix transformations
  • Alternate coordinate systems (bases)

Calculus (Differential calculus, multivariate calculus)

  • Limits and continuity
  • Derivatives, Differentiations
  • Derivatives Applications
  • Equations

Training Dates

April 14 - May 6, 2018

Times: Every Sat & Sun; 8:00 AM - 10:00 AM (Pacific Standard Time)

Please check local date and time

Each session will be recorded and the recordings will be shared after each session with students

Refund Policy

1. There are no refunds.
2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212.

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