How do I get into machine learning PHD program?

You’ll need an undergraduate degree in a quantitative subject such as physics, maths, computer science, or engineering where you should have covered probability and statistics, multivariable calculus, and linear algebra. You’ll also need to know how to program, either from courses or teaching yourself.

Where can I learn convex optimization?

Purdue University. This graduate-level course introduces students to the basics of convex analysis and convex optimization. It covers basic algorithms of convex optimization and applications in aerospace engineering.

How do I get started with machine learning research?

How Do I Get Started?

1. Step 1: Adjust Mindset. Believe you can practice and apply machine learning.
2. Step 2: Pick a Process. Use a systemic process to work through problems.
3. Step 3: Pick a Tool. Select a tool for your level and map it onto your process.
4. Step 4: Practice on Datasets.
5. Step 5: Build a Portfolio.

Is convex optimization polynomial time?

Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard. With recent advancements in computing and optimization algorithms, convex programming is nearly as straightforward as linear programming.

Are there efficient algorithms for non-convex optimization problems?

Algorithms and analysis for non-convex optimization problems in machine learning This dissertation proposes efficient algorithms and provides theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning.

Why study machine learning and big data at UW?

The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation.

What is Extreme Learning Machine (ELM)?

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights.