printer friendly pageCME 107: Introduction to Machine Learning (EE 104)
Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
ENGR 108;
EE 178 or
CS 109; CS106A or equivalent.
Terms: Spr
| Units: 3-5
Instructors:
Lall, S. (PI)
EE 104: Introduction to Machine Learning (CME 107)
Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
ENGR 108;
EE 178 or
CS 109; CS106A or equivalent.
Terms: Spr
| Units: 3-5
Instructors:
Lall, S. (PI)
EE 109: Digital Systems Design Lab
The design of integrated digital systems encompassing both customized software and hardware. Software/hardware design tradeoffs. Algorithm design for pipelining and parallelism. System latency and throughput tradeoffs. FPGA optimization techniques. Integration with external systems and smart devices. Firmware configuration and embedded system considerations. Enrollment limited to 25; preference to graduating seniors. Prerequisites: 108B, and
CS 106B or X.
Terms: Spr
| Units: 4
Instructors:
Olukotun, O. (PI)
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