In recent years, there has been growing interest in ConvNet-parametrized energy-based generative models. The concomitant need for representation, generation, efficiency and scalability in generative models is addressed by the framework of ConvNet-parametrized EBMs. Specifically, different from existing popular generative models, such as Generative Adversarial Nets (GANs) and Variational Auto-encoders (VAEs), the energy-based generative model can unify the bottom-up representation and top-down generation into a single framework, and can be trained by "analysis by synthesis", without recruiting an extra auxiliary model. Both model parameter update and data synthesis can be efficiently computed by back-propagation. The model can be easily designed and scaled up. The expressive power and advantages of this framework has launched a series of research works leading to significant theoretical and algorithmic maturity. Due to its major advantages over conventional models, energy-based generative models are now utilized in many computer vision tasks. The tutorial will provide a comprehensive introduction to energy-based generative modeling and learning in computer vision. An intuitive and systematic understanding of the underlying learning objective and sampling strategy will be developed. Different types of computer vision tasks successfully solved by the energy-based generative frameworks will be presented. Besides introducing the energy-based framework and the state-of-the-art applications, this tutorial will aim to enable researchers to apply the energy-based learning principles in other contexts of computer vision.