CS486/686: Introduction to Artificial Intelligence


Winter 2019


People:


Communication


Deliverables (Assignment submissions and grades)


TIMETABLE:

Lectures will take place twice per week as follows

Exams:

Office Hours are as follows:

STRUCTURE

The course will consist of two 1.5-hour in-class sessions per week.

The course content will be delivered in a lecture format, with four assignments, a midterm, and a final exam. Graduate students must complete a project (optional for undergraduates).


READINGS:

Primary Texts:

David Poole and Alan Mackworth "Artificial Intelligence: Foundations of Computational Agents". Cambridge University Press, (1st edition: 2010, 2nd edition: 2017).
(available online. The section references below are to the 2nd edition.)
And the useful and informative java applets on the CI-Space website

Secondary Readings:

Russell and Norvig Artificial Intelligence
Ian Goodfellow and Yoshua Bengio and Aaron Courville Deep Learning

Assessment

For CS486 students:

For CS686 (grad) students:

How and Where to submit


Course Objectives

The design of automated systems capable of accomplishing complicated tasks is at the heart of computer science. Abstractly, automated systems can be viewed as taking inputs and producing outputs towards the realization of some objectives. In practice, the design of systems that produce the best possible outputs can be quite challenging when the choice of outputs is constrained, the consequences of the outputs are uncertain and/or dependent on other systems, the information provided by the inputs is incomplete and/or noisy, there are multiple (possibly competing) objectives to satisfy, the system must adapt to its environment over time, etc. This course provides an introduction to Artificial Intelligence, covering some of the core topics that underly automated reasoning. The modeling techniques that will be covered are quite versatile and can be used to tackle a wide range of problems in many fields including natural language processing (e.g., topic modeling, document clustering), robotics (e.g., mobile robot navigation), automated diagnosis (e.g., medical diagnosis, fault detection), data mining (e.g., fraud detection, information retrieval), operations research (e.g., resource allocation, maintenance scheduling), assistive technologies, human-computer interaction, etc.

See the official official course outline

Course Topics

  1. Agents and Abstraction
  2. States and Searching
  3. Features and Constraints
  4. Propositions and Inference
  5. Reasoning under uncertainty
  6. Supervised Learning
  7. Unsupervised Learning
  8. Reinforcement Learning
  9. Machine Learning
  10. Neural Networks and Deep Learning
  11. Planning under certainty
  12. Planning under uncertainty
  13. Additional topics if time permits
(not necessarily covered in this order)

COURSE SLIDES

The lecture slides and schedule will be finalised as the course progresses.
  1. January 7th, 2019: Introduction Slides (88kb) (6-up version (118Kb) )
    Readings: Poole and Mackworth (2nd Ed.) 1.1
  2. January 9th, 2019: What is AI? Slides (52Mb) (6-up version (24Mb))
    Readings: Poole and Mackworth (2nd Ed.) 1.1-1.2
  3. January 14th, 2019: Agents and Abstraction Slides (77Mb) (6-up version (33Mb))
    Readings: Poole and Mackworth (2nd Ed.) 1.3-1.10, 2.1-2.3
  4. January 16th, 2019: States and Searching Slides (1Mb) (6-up version (1.7Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 3 (all)
  5. January 21st, 2019: Features and Constraints Slides (0.5Mb) (6-up version (1.2Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 4.1-4.8
  6. January 23rd, 2019: Propositions and Inference Slides (0.4Mb) (6-up version (1.5Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 5.1-5.3, and Chapt. 13.1-13.2
  7. January 30th, 2019: Planning under certainty Slides (3Mb) (6-up version (3.1Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 6.1-6.4
  8. January 30th, 2019: Supervised Learning I Slides (0.4Mb) (6-up version (1 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 7.1-7.3.1,7.4
  9. February 11th, 2019: Supervised Learning II Slides (0.5Mb) (6-up version (1 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 7.3.2,7.5-7.6
  10. February 13th, 2019: Reasoning under Uncertainty I Slides (1.5Mb) (6-up version (2.4 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 8.1-8.4
  11. February 25th, 2019: Reasoning under Uncertainty II Slides (3.1Mb) (6-up version (3.0 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 8.5-8.9
  12. March 11th, 2019: Learning with Uncertainty I Slides (0.25Mb) (6-up version (0.55 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 10.1,10.4
  13. March 13th, 2019: Learning with Uncertainty II Slides (0.52Mb) (6-up version (0.7 Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 10.2,10.3,10.5
  14. March 18th, 2019: Planning under Uncertainty I Slides (0.42Mb) (6-up version (0.89Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 9.1-9.3
  15. March 20th, 2019: Planning under Uncertainty II Slides (0.91Mb) (6-up version (1.52Mb))
    Readings: Poole and Mackworth (2nd Ed.) Chapt. 9.5

ASSIGNMENTS

Posted assignments with firm dates:
  1. Assignment 1 Due January 29th, 2019 at 5pm (in LEARN dropbox for assignment 1). For TA office hours see above under office hours.
  2. Assignment 2 and datasets for Q2. Due February 15th 16th, 2019 at 5pm. (in LEARN dropbox for assignment 2).
  3. Assignment 3 and datasets for Q2 (these are the same as for assignment 2). Due March 11th 13th 18th, 2019 at 5pm 12pm (in LEARN dropbox for assignment 3).
  4. Assignment 4 and data+code. Due April 5th, 2019 at 5pm (in LEARN dropbox for assignment 4).
Upcoming assignments with tentative dates:

OTHER MATERIAL (videos, software, handouts, etc)


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