If you’re going to work on a particular application of machine learning such as vision or speech recognition, there are a few things to bear in mind. It doesn’t matter too much which application you choose as long as you’re building skills in the underlying machine learning methods. But it’s still worth putting some thought into which application to work on. The ideal is an area where progress is being made but hasn’t levelled off yet. You can find these areas by looking at this article , which has graphs showing the progress of machine learning in many specific applications.
Advanced Single-Carrier and Multi-Carrier OFDM transceivers (Transmission protocols, Frequency Diversity, Optimal selection of OFDM parameters, OFDM-based multiple access schemes). Advanced Multiple-Antenna Techniques (Diversity-Multiplexing Tradeoff, Hybrid MIMO systems, MIMO-OFDM). Relaying and Cooperative Communications (Types of relaying, architectures, performance). Spectrum Management (Cognitive Networks, Sensing and Allocation Algorithms, Carrier Aggregation). LTE-Advanced (Evolution from 3G to 4G and beyond, Targets and IMT-Advanced Requirements, LTE Radio Access, Supported Transmission Modes). Towards 5G (HetNets, Small-cells). Wi-Fi ( family of standards, Super G Technology). Wired Standards (ADSL-VDSL). Satellite communication standards (DVB-S2, DVB-S2X extension, Hybrid terrestrial/satellite networks and applications).