[100% Off] Machine Learning (MASTER Degree) Udemy Coupon
Machine Learning (MASTER Degree)
Machine Learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such as Microsoft’s Kinect, Amazon’s recommender system, the spam detection systems of Facebook, and the advertising engines of these and many other companies. ML is the key enabling technology behind face detection in consumer cameras, news personalization, book and movie recommender systems, image and video search, credit card fraud detection, speech recognition systems, and many more applications that most people have begun to take for granted. ML has also begun to make it possible to have automatically-driven cars, more efficient energy management systems, and improved systems for health-care management.
Academically, ML is one of the fastest growing fields in all fronts: Theory, methodology and application. ML for historical reasons is strongly connected to computer science and statistics departments in North America. However, it is also revolutionizing biology, astrophysics, engineering, and all other areas of science. ML innovations, such as boosting and SVMs among others, have strongly impacted statistics in recent years, and the interplay of statistics and ML has left us with tools such as random forests (a key component of the kinect sensor). Tools from bandits and reinforcement learning are impacting operations research in business and health-care.
- Introduction to machine learning.
- Linear prediction.
- Maximum likelihood and linear prediction.
- Ridge, nonlinear regression with basis functions and Cross-validation.
- Bayesian learning
- Gaussian processes for nonlinear regression
- Bayesian optimization, Thompson sampling and bandits.
- Decision trees.
- Random forests.
- Spring break.
- Random forests applications: Object detection and Kinect.
- Unconstrained optimization: Gradient descent and Newton’s method.
- Logistic regression, IRLS and importance sampling.
- Neural networks.
- Deep learning with autoencoders.
- Importance sampling and MCMC.
- Constrained optimization, Lagrangians and duality.
- Application to penalized maximum likelihood and Lasso.
Instructors: Didaktik ACADEMY