Machine Intelligence

University of Newcastle

Course Description

  • Course Name

    Machine Intelligence

  • Host University

    University of Newcastle

  • Location

    Newcastle, Australia

  • Area of Study

    Computer Info Systems, Computer Programming, Computer Science

  • Language Level

    Taught In English

  • Prerequisites

    SENG1120 MATH1510 MATH1110

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • Host University Units

    10
  • Recommended U.S. Semester Credits
    3 - 4
  • Recommended U.S. Quarter Units
    4 - 6
  • Overview

    This course provides an overview about important past and current developments, concepts, and applications in the fast evolving field of machine intelligence. It is an introductory course and could later be extended by higher studies in areas such as, advanced machine learning, data mining, bioinformatics, image processing, optimisation, autonomous agents, computer vision, computer graphics, and related fields. The course's topic is a central part of computer science and software engineering. Many of the concepts addressed by this course were initially biologically motivated and fall under the umbrella of brain theory. The aim is to get an understanding of intelligence, learning, memory, language, and the workings of the human brain by modelling and implementing aspects of these concepts in the computer. With the availability of faster workstations and sophisticated robotic hardware, machine intelligence methods can find more widespread applications. This course will address several applications and systems where machine intelligence methods lead to significant advancements, often surprising solutions, and sometimes triumphal success.
    LEARNING OUTCOMES
    1. apply Artificial Intelligence (AI) techniques;
    2. demonstrate their understanding and apply examples of machine learning methods.
    3. explain past and current developments in machine intelligence.
    4. demonstrate the ability to project towards future developments of the field including possible ethical implications in areas such as data mining and robotics.
    CONTENT
    Machine Learning
    Automated Reasoning and Logic
    Search and Prediction in Games
    Neural Networks and Brain Mechanisms
    Evolutionary Algorithms
    Adaptive Robotics
    ASSESSMENT ITEMS
    Written Assignment: History and Philosophy Assignment
    Written Assignment: Homework Assignment 1
    In Term Test: Mid-semester Exam
    Written Assignment: Homework Assignment 2
    Formal Examination: Formal Examination *
    * This assessment has a compulsory requirement.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.