Artificial Intelligence & Machine Learning Articles

COMP_SCI 348: Intro To Artificial Intelligence

VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Students must have taken [CS 111 and (CS 214 or be a CogSci major)] or be a Computer Science Masters or PhD student, or obtain instructor permission, in order to register for this course. Stat 304 is *not* a substitute for Comp_Sci 214. Description

Core techniques and applications of artificial intelligence. Representation retrieving and application of knowledge for problem solving, planning, probabilistic inference, and natural language understanding.

This course satisfies the AI Breadth Requirement.

OPTIONAL TEXTBOOK: Russell & Norvig , Artificial Intelligence: A Modern Approach , Prentice Hall, 3rd edition

COURSE INSTRUCTOR: Mohammed A. Alam or Prof. Birnbaum or Prof. Edith ElkindCOURSE COORDINATOR: Prof. Kristian Hammond

COURSE GOALS: The goal of this course is to expose students to the basic ideas, challenges, techniques, and problems in artificial intelligence. Topics include strong (knowledge-based) and weak (search-based) methods for problem solving and inference, and alternative models of knowledge and learning, including symbolic, statistical and neural networks.

DETAILED COURSE TOPICS:

Philosophical foundations of artificial intelligence

Intelligent agents

Search, including A*, iterative deepening

Logical formalisms, propositional and first order predicate calculus

Planning, from STRIPS to Partial Order Planning

Probability & uncertainty, including Bayesian inference and Bayes networks

Machine learning, including decision trees, neural nets, hill climbing, genetic algorithms

COURSE OBJECTIVES: After this course, students should be able to

Articulate key problems, both technical and philosophical, in the development of artificial intelligence

Teach themselves more about AI through reading texts and research articles in the field

Apply AI techniques in the development of problem-solving and learning systems

Artificial Intelligence

Course planning information Course notes

The final examination will be an online supervised examination using remote invigilation.

All assessments are compulsory.

You need to complete the above course or courses before moving onto this one.

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You cannot enrol in this course if you have passed (or are enrolled in) any of the course(s) above as these courses have similar content or content at a higher level.

General progression requirements You must complete at least 45 credits from 200-level before enrolling in 300-level courses.

1 Describe key Artificial Intelligence techniques and problem-solving methods.

2 Design and implement computer programs to store and manipulate knowledge for Artificial Intelligence applications.

3 Describe and implement Artificial Intelligence techniques for a range of target problems.

4 Apply logic systems for inference and control problems.

5 Identify the advantages and disadvantages of applying various Artificial Intelligence techniques for solving real-world problems.

Learning outcomes can change before the start of the semester you are studying the course in.

Assessments

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types Computer programmes Computer animation and screening, design, programming, models and other computer work. Creative compositions Animations, films, models, textiles, websites, and other compositions. Exam College or GRS-based (not centrally scheduled) An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format. Exam (centrally scheduled) An exam scheduled by Assessment Services (centrally) – you'll usually be told when and where the exam is through the student portal. Oral or performance or presentation Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations. Participation You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on. Portfolio Creative, learning, online, narrative, photographic, written, and other portfolios. Practical or placement Field trips, field work, placements, seminars, workshops, voluntary work, and other activities. Simulation Technology-based or experience-based simulations. Test Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school. Written assignment Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments. Textbooks needed

There are no set texts for this course.

Engineering Science (Artificial Intelligence) MS

This Engineering Sciences MS with a course focus on Artificial Intelligence (AI) is a multidisciplinary program designed to train students in the areas of machine learning, programming languages, deep learning algorithms, and advanced artificial neural networks that use predictive analytics to solve real-world problems. Students in this program take foundational courses in AI and can choose from elective concentrations like data analytics, computational linguistics and information retrieval, machine learning and computer vision, knowledge representation and robotics.

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