Welcome to the graduate course on Systems and Machine Learning! This is a project-based seminar course covering topics on Systems for Machine Learning and Machine Learning for Systems.

Machine learning is transforming several domains ranging from natural language processing to drug discovery today. One of the key factors that enabled rapid progress in ML/AI in recent years has been fast-evolving underlying hardware and software platforms. In this course, we will cover recent advancements in research and industry on machine learning systems that enabled the AI/ML revolution. Specific topics include domain-specific architectures, deep learning frameworks and compilers, networking and scheduling in deep learning clusters, etc. We will also discuss practical challenges in deploying such systems. In the second half of the course, we will explore how machine learning has been employed to tackle various networking and systems challenges such as Internet congestion control, adaptive bitrate selection in video streaming, flow prediction, etc.

Instructor: Sangeetha Abdu Jyothi

Class Hours: Tue Thu 3:30 - 4:50 pm PT (fully asynchronous, discussions on Piazza)

Office Hours: Wed 3:30 - 4:30 pm PT or by appointment

Piazza: https://piazza.com/uci/winter2021/cs295/home

Course Policies: Course Policies

Prerequisites: Understanding of basic concepts in machine learning and systems (taken at least one undergrad course in ML and (networking or distributed systems))

Videos are available on the "Modules" page.

Schedule: (more optional papers will be added as the quarter progresses)

Date	Category	Lecture	Required Reading	Optional
1/5/2021		Lec 1: Introduction
1/7/2021	Systems For ML	Lec 2: Domain Specific Architectures	In-Datacenter Performance Analysis of a Tensor Processing Unit (ISCA'17)	A Configurable Cloud-Scale DNN Processor for Real-Time AI (ISCA'18)
1/12/2021	Systems For ML	Lec 3: DL Frameworks	TensorFlow (OSDI'16) PyTorch (NeurIPS'19)	MXNet CNTK (KDD'16)
1/14/2021	Systems For ML	Lec 4: DL compilers	TVM (OSDI'18)	Glow nGraph XLA TensorComprehensions
1/19/2021	Systems For ML	Lec 5: Networking Challenges in DNN training	Parameter Server (OSDI'14) Horovod (arXiv'18)	BytePS (OSDI'20) TicTac (MLSys'19) P3 (MLSys'19)
1/21/2021	Systems For ML	Lec 6: Cluster scheduling for DL workloads	Gavel (OSDI'20)	AntMan (OSDI'20) Themis (NSDI'20) Gandiva (OSDI'18)
1/26/2021	Systems For ML	Lec 7: Automated ML: Hyperparameter Tuning and Neural Architectural Search (NAS)	Hyperband (JMLR'18) Designing Neural Architectures using RL (ICLR'17)	Cerebro (VLDB'20) Vizier (KDD'17) ASHA (MLSys'20)
1/28/2021	Systems For ML	Lec 8: Scaling Gradient Boosting and RL	XGBoost (KDD'16) Asynchronous Methods for Deep Reinforcement Learning (ICML'18)	RLlib (ICML'18)
2/2/2021	Systems For ML	Lec 9: ML at the Edge	Towards Federated Learning at Scale: System Design (MLSys'19)
2/4/2021	Systems For ML	Lec 10: Challenges in Operational ML Systems	Hidden Technical Debt in Machine Learning Systems (NeurIPS'15) Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective (HPCA'18)	TFX (KDD'17)
2/9/2021	ML for Systems	Lec 11: Deep RL and Challenges in the real world	Deep RL tutorial (background reading, not for review) Challenges of Real-World Reinforcement Learning (ICML'19)
2/11/2021	ML for Systems	Lec 12: Deep RL for Congestion Control	Aurora (ICML'19)
2/16/2021	ML for Systems	Lec 13: Deep RL for Video Streaming (ABR control)	Pensieve (SIGCOMM'17)
2/18/2021	ML for Systems	Lec 14: Classic control + Learning (congestion control & ABR)	Learning in-situ (ATC'20) Classic Meets Modern (SIGCOMM'20)
2/23/2021	ML for Systems	Lec 15: Deep RL for Scheduling	Placeto (NeurIPS'19)	Decima (SIGCOMM'19) RLScheduler (SC'20)
2/25/2021	ML for Systems	Lec 16: ML for Index Structures	Learned Index Structures (SIGMOD'18)
3/2/2021	ML for Systems	Lec 17: ML for CDN cache replacement	Learning Relaxed Belady (NSDI'20)
3/4/2021	ML for Systems	Lec 18: ML for flow prediction	Flux (NSDI'19)
3/9/2021	ML for Systems	Lec 19: Interpretability of RL-based controllers	Metis (SIGCOMM'20)
3/11/2021		Final Project Presentations

CS 295: SYSTEMS AND ML