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Our Courses
Recommender Systems
In this course you will: a) understand the basic concept of recommender systems. b) understand the Collaborative Filtering. c) understand the Recommender System with Deep Learning. d) understand the Further Issues of Recommender Systems. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, conditional probability, and basic machine learning algorithms.
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Course by
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English
Machine Learning Basics
In this course, you will: a) understand the basic concepts of machine learning. b) understand a typical memory-based method, the K nearest neighbor method. c) understand linear regression. d) understand model analysis. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
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Course by
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Self Paced
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15 hours
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English
Motion Planning for Self-Driving Cars
Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws.
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Course by
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Self Paced
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32 hours
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English
Machine Learning Algorithms
In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
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Course by
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Self Paced
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16 hours
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English
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
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Course by
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31 hours
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English
State Estimation and Localization for Self-Driving Cars
Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car.
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Course by
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Self Paced
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27 hours
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English
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars.
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Course by
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Self Paced
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35 hours
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English