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Professional Development Certificates
Certificates
Professional Development Certificate in Applied Artificial Intelligence
This advanced and practical non-credit professional development certificate program is designed to equip professionals with actionable industry-relevant knowledge and skills required to be fully functional data scientists and Al developers.





Admission for the Fall 2021 semester opens on February 1, 2021 and closes August 25, 2021.
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Professional Development Certificate in Applied Artificial Intelligence

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Information Session - PDC in Data Analytics, AI, Machine Learning

Information Session - Career Pathways in Data Analytics, AI and Machine Learning

Join us for the Career Pathways in Data Analytics, AI and Machine Learning information session to explore trends, challenges, and career opportunities in this fast-growing field.

 

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PDC Online Information Session

Online Information Session

Wondering if a McGill Professional Development Certificate is for you? Get answers from our on-demand info session and learn about topics offered, delivery method, our approach to learning and teaching, tuition fees and how to apply. Plus, hear from actual students who will talk about their experience and how the program they chose helped them achieve their professional aims.

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About this program
Description
In-class, Part-time

This advanced and practical non-credit professional development certificate program is designed to equip professionals with actionable industry-relevant knowledge and skills required to be fully functional data scientists and Al developers. The program aims to develop the skills required to evaluate, design, develop and improve Al algorithms through hands-on projects and problem solving. Participants are expected to develop a portfolio of Al projects during the course of the program.

The program consists of two required courses and three complementary courses. 

The program is offered in English and must be completed within 2 years.

Program Toolkit
  • Python
  • Jupyter Notebook
  • Pytorch, Tensorflow and Keras
  • Git and GitHub
  • Cloud computing services and infrastructure (such as GCP, AWS or Azure)
  • OpenAI Gym
Admission Requirements

McGill's School of Continuing Studies Professional Development Certificate in Data Science and Machine Learning;

OR

A) A minimum of a Bachelor's (or higher) degree or equivalent in one of the following areas: Engineering, Computer Science, Software Engineering, Mathematics, Statistics or a related discipline;

B) As well as Prior experience in Machine Learning and strong programming skills in Python.

C) And an online Skills Assessment Test in Machine Learning & Python OR YCBS 255 Statistical Machine learning course.

Note:

Applications from mature students (21 years of age or older) who do not meet the above criteria but have extensive and relevant experience in software development, machine learning, data science, and have previously completed relevant coursework in calculus, statistics, machine learning will be evaluated on a case by case basis.

 

Learning Outcomes

Upon completion of this program, you should be able to:

  • Evaluate and select the appropriate algorithm or model to solve an Al problem
  • Analyze errors and design strategies to solve them
  • Integrate recent advancements in machine learning and artificial intelligence in the data analysis process
  • Apply advanced strategies and methods to optimize machine learning models
  • Design and construct software systems that implement machine learning models and artificial intelligence algorithms.
Who Should Attend

This program is most suitable for professionals who have already completed McGill's School of Continuing Studies Professional Development Certificate in Data Science and Machine Learning and who are seeking to take their machine learning skills to the next level. It is also suitable for software developers with prior experience in data science, machine learning and strong programming skills in Python.

Application Deadlines

The application deadlines for this program are as follows:

  • Fall Admission: August 25, 2021

  • Winter 2021 Admission: March 1, 2021

Language Requirements

To be admitted to a Professional Development Certificate, proof of English proficiency is not required. However, since the language of instruction is English, a good command of English is required in order to perform well in courses and complete the program.

Sessions for Admission

This program admits new students in the following semesters:

  • Fall 

  • Winter 

Please make sure to verify application deadline and apply before the deadline for that session. 

Financial Aid and Awards

Professional Development Certificates are non-credit programs and as such, are not eligible for financial aid or awards such as Quebec’s loans and bursaries program (AFE).  

In many cases, employers will cover the cost courses of McGill’s Professional Development Certificate courses when the subject matter applies directly to the employees work. Please check with your employer to learn more about their policies regarding professional development and verify the funds made eligible for your professional development needs by your organization.

Additionally, in some cases, financial institutions may offer personal loans to cover the costs or professional development programs. Please check with your bank to find out if you are eligible. 

Please note that tuition fees paid for courses as part of Professional Development Certificates are also eligible for official tax receipts (T2202A) and Relevé 8).

Tuition and Fees

For Professional Development Certificates, fees are paid per course at the time of registration. Please find the cost per course using the course list on this page and click on Learn More in the description box of each course. 

To submit your application to a Professional Development Certificate program, a non-refundable fee of $92.30 is required. 

How to Apply

Apply to this program via Athena. 

If you need help with the application procedure, please find detailed instructions on how to apply at mcgill.ca/continuingstudies/how-apply-professional-programs.

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Courses

Non-Credit Courses
2 Required Courses (12 CEUs)
YCNG 228 Predictive & Classification Modelling (6 CEUs)
Predictive modelling is a process that uses data and statistics to predict outcomes using data models. These models are used to detect fraud, optimize marketing campaigns, reduce risk, manage resources and improve operations. This course will familiarize students with how to design experiments and optimize ML models and interpret their output. Students will test hypotheses by using different techniques, design experiments, use machine learning algorithms to evaluate the output of different machine learning models, and explore methods to increase precision and/or accuracy.
Learn more >
YCNG 229 Neural Networks & Deep Learning (6 CEUs)
Artificial neural networks are a set of algorithms, inspired by the way human brain processes information, which are designed to recognize patterns. Deep learning is one of machine learning methods based on artificial neural networks. Neural networks and deep learning offer the most powerful techniques to deal with different aspects of data science such as natural language processing, computer vision and time series analysis. Building on the knowledge and skills acquired during YCBS 258 Practical Machine Learning course, this course will focus on the practical application of neural network models with the objective to develop students’ ability to implement them using Python and Keras.
Learn more >
3 Complementary Courses (17-18 CEUs)
YCNG 230 Intelligent Agents & Reinforcement Learning (6 CEUs)
Intelligent agents are programs that can be applied to autonomously solve real-world optimization and planning problems, as well as help deal with incomplete information or uncertain environments. Reinforcement Learning is an approach to build goal-oriented intelligent agents, which help find strategies to optimize a desired outcome. In this course, students will explore the development of intelligent agents using different techniques, algorithms and approaches; the design and implementation of systems that exhibit intelligent behaviour through an end-to-end project; practical application of the most current programming tools, search methods, knowledge representation using logic and probability, planning and decision making under uncertainty and constraint satisfaction problems; reinforcement learning fundamentals including design of intelligent agents and integration with deep learning.
Learn more >
YCNG 231 Deep Learning for Computer Vision (6 CEUs)
Computer vision is one of the fields of study, which has benefited tremendously from the latest advancements in artificial intelligence. Computer vision is concerned with helping machines learn to “see” and understand the context of their environment. It involves acquiring, processing, transforming, modifying, analyzing and understanding digital images or videos. The result is the extraction of data needed to understand the content of digital images, infer something from the environment, thus allowing complex decisions to be made. In this course, participants will learn more about computer vision from acquisition of image data to complex decision-making using deep learning methods and techniques. Image acquisition through sensors; feature detection; image classification, detection, segmentation. Convolutional Neural Networks (CNN) architectures and their applications. Most current computer vision applications, libraries and image databases will be explored.
Learn more >
YCNG 232 Natural Language Processing Fundamentals (6 CEUs)
Natural Language Processing (NLP) is a subfield of artificial intelligence concerned with the ability of computers to process, understand and interpret human languages. Deep learning has revolutionized NLP by improving many aspects of the field since its transition from classic linguistic approaches. This course will provide an overview of Natural Language Processing fundamentals such as language models, word embeddings, Recurrent Neural Networks (RNNs), wide variety of applications of neural networks in NLP, intrinsic and extrinsic evaluation, most current NLP tools.
Learn more >
YCNG 233 Time Series Analysis Fundamentals (6 CEUs)
Time series is an area of machine learning concerned with the analysis of series of data points ordered in time. Time series analysis is used in many fields to predict trends: from industrial machinery data, smart home, precision agriculture, cyber security, customer usage/engagement, marketing, asset management, finance, etc. Due to the temporal aspect of the data, time series data require specific preprocessing, feature engineering, algorithms, and validation strategy. This course will dive into the fundamentals of applying artificial intelligence techniques on time series data, statistical, machine and deep learning models for time series. Emphasis is placed on the main applications of time series analysis: forecasting, clustering and anomaly detection. Data science techniques applicable to time series. Deep learning methods, rolling predictions, online learning, backtesting.
Learn more >
YCNG 234 Internet of Things (5 CEUs)
The Internet of Things (IoT) is an important source of relevant data for different industries. It permits to collect and process information about the physical world in real time. This course will examine applications of IoT, the fundamental technologies that support IoT devices and the transmission, processing, the analysis of the data they generate, and the fundamental software and hardware technologies that support IoT devices; data transmission protocols; tools to process the data generated by IoT devices; data analysis techniques that enable common applications related to IoT devices.
Learn more >
YCNG 235 Recommender Systems (6 CEUs)
Have you ever wondered how the message: “Customers like you also purchased the following items…” appears on your screen when you are shopping online? Recommender Systems use machine learning algorithms that help users discover new products and services. There exists a wide spectrum of recommender systems applications that help users choose movies, restaurants, music to listen to, jobs to apply to, products to purchase, social media profiles, among many others. This course will explore fundamental concepts and techniques in recommender systems: similarity models, non-personalized, content-based, and hybrid systems; association rules mining; collaborative filtering: user-, item-, and graph-based models; matrix factorization; graph recommenders, sequential recommenders, evaluation of recommender systems.
Learn more >

Contacts

Career and Professional Development
(Non-credit courses and programs)

Phone: +1 514-398-5454
688 Sherbrooke Street West, Suite 1029 Montreal, Quebec, Canada H3A 3R1
Hours of Operation

Monday to Friday
9:00 a.m. to 5:00 p.m.

Professional associations

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