Google Code for Remarketing Tag - Bloom
Ă€ propos de ce programme
Ce programme pratique et avancé de perfectionnement professionnel non crédité, offert en ligne et en classe, est conçu pour donner aux participants des connaissances et des compétences concrètes et pertinentes pour l’industrie, afin de faire d’eux des experts en science des données et des développeurs en intelligence artificielle (IA) performants. Le programme vise à développer des compétences d’évaluation, de conception, d’élaboration et d’amélioration d’algorithmes d’IA par l’entremise de projets pratiques et de résolution de problèmes. Les participants élaboreront un portfolio de projets en IA. Le programme est offert en anglais et doit être terminé en deux ans.
Ce programme est offert en anglais et doit être complété dans deux ans.
Certificat de perfectionnement professionnel en science des donnĂ©es et apprentissage automatique de l’École d’éducation permanente de l’UniversitĂ© Ŕ¦°óSMÉçÇř;
OU
A) Diplôme de baccalauréat (ou supérieur) ou formation équivalente dans l’un des domaines suivants : génie, informatique, génie logiciel, mathématiques, statistiques ou discipline connexe.
B) Et l'expérience antérieure en apprentissage automatique et solides compétences de programmation dans Python.
C) Et un test de compétences en ligne sur l’apprentissage automatique et sur Python OU cours YCBS 255 Apprentissage statistique automatique.
Note :
Les étudiants d’âge adulte (21 ans et plus) qui ne répondent pas aux critères ci-dessus, mais qui ont une expérience significative et pertinente en développement de logiciels, en apprentissage automatique ou en science des données et qui ont déjà réussi des cours pertinents en calcul, en statistiques ou en apprentissage automatique, verront leurs demandes évaluées au cas par cas.
Ce programme est surtout pertinent pour les professionnels qui ont dĂ©jĂ suivi le Certificat de perfectionnement professionnel en science des donnĂ©es et apprentissage automatique de l’École d’éducation permanente de l’UniversitĂ© Ŕ¦°óSMÉçÇř et qui dĂ©sirent pousser plus loin leurs compĂ©tences en apprentissage automatique. Il convient Ă©galement aux dĂ©veloppeurs de logiciels ayant de l’expĂ©rience en science des donnĂ©es ou en apprentissage automatique ainsi que de solides compĂ©tences de programmation dans Python.
Ă€ la fin du programme, vous pourrez :
- évaluer et choisir l’algorithme ou le modèle approprié pour résoudre un problème lié à l’IA;
- analyser les erreurs et concevoir des stratégies pour les résoudre;
- intégrer les récentes avancées en apprentissage automatique et en intelligence artificielle dans le processus d’analyse des données;
- mettre en pratique des stratégies et des méthodes avancées pour optimiser les modèles d’apprentissage automatique;
- concevoir et créer des systèmes logiciels intégrant des modèles d’apprentissage automatique et des algorithmes d’intelligence artificielle.
Vous trouverez toute l’information sur les droits de scolarité et les autres frais pour ce programme, notamment pour les formules à temps partiel et à temps plein, à l’adresse mcgill.ca/continuingstudies/fr/certificats-de-perfectionnement-professionnel-frais-de-cours.
Comme les programmes de certificat de perfectionnement professionnel ne donnent pas droit à des unités, ils ne sont pas admissibles à l’aide financière ni aux bourses comme ceux du programme de prêts et bourses (Aide financière aux études) du Québec.
Veuillez aussi noter que des reçus fiscaux officiels (formulaire T2202A et relevé 8) peuvent être fournis pour les droits de scolarité associés aux cours de ces programmes.
Cours
2 Cours requis (12 UEC)
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.
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.
3 Cours complémentaires (17-18 UEC)
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.
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.
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.
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.
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.
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.
Contacts
Career and Professional Development
(Non-credit courses and programs)
(Non-credit courses and programs)
Phone: +1 514-398-5454
680 Sherbrooke Street West, Suite 1029
Montreal, Quebec, Canada H3A 2M7
Horaires d'ouverture
Monday to Friday
9:00 a.m. to 5:00 p.m.